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共 3332 条记录，以下是71-80 订阅

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An Intelligent Forecasting Classification Model for Traffic Analysis using machine learning Techniques 4

An Intelligent Forecasting Classification Model for Traffic ...

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

作者： Kishor, Bodla Tiwari, Rajesh Patra, Raj Kumar Pinjakar, Latika Singh, Mahesh Cmr Engineering College Dept. of Computer Science and Engineering Hyderabad India Cmr Technical Campus Dept. of Computer Science and Engineering Hyderabad India Symbiosis Institute of Technology Dept. of Computer Science and Engineering Nagpur India Shri Shankaracharya Technical Campus Dept. of Eee Durg Bhilai India

ISBN: (纸本)9798350343670

In addition to taking care of the normal citizens, Road Traffic protection is also one of the main issue for corporations governing the transportation. We use data mining techniques to get the hidden matter from the database. the applications of data mining is there in various fields such as fraud detection, marketing and many more. Many countries were affected due to many decisions that were taken by the governments one such issue is globalization. this has brought some drastically changes in the intake level and the financial related activities and this has given a boost to transportation department. Understanding the importance of the street protection the authorities are putting effort to know the reasons behind the street accidents. the increase in the street injuries made it difficult to determine the limitations of the street injuries. this paper explains the street injuries frequency from analysing the database. this work is about 95% efficient. © 2024 IEEE.

关键词： classification data mining machine learning traffic analysis Transportation models

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Intelligent In-Car Emotion Regulation Interaction System Based on Speech Emotion recognition 4

Intelligent In-Car Emotion Regulation Interaction System Bas...

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4th international conference on Computer, Control and Robotics (ICCCR)

作者： Yang, Yuhan Zhang, Yan Zhong, Zhinan Dai, Wan Chen, Yunfei Chen, Mo Southeast Univ Sch Mech Engn Nanjing Peoples R China Nanjing Tech Univ Coll Art & Design Nanjing Peoples R China

ISBN: (纸本)9798350373141;9798350373158

With driving emerging as a common mode of transportation, the automotive industry has increasingly prioritized driving safety and experience. A substantial body of research focused on driving safety and the overall travel experience has underscored the pivotal role of emotions. In this article, we introduce an innovative in-car emotion recognition and interaction system, carefully crafted to intelligently respond to the emotional states of drivers. this system captures real-time emotional data through its user input layer and seamlessly integrates it into the technological architecture layer, residing within the vehicle's CPU. Leveraging cutting-edge deep learning models for emotion recognition, the system's outcomes trigger tailored emotion regulation strategies within the interaction feedback layer. Notably, our study introduces a groundbreaking speech fusion feature, MFCCs+, meticulously crafted for driving contexts. Furthermore, we have optimized the driving speech emotion recognition model using 1D-CNN, resulting in a remarkable 10% improvement in recognition accuracy. Subsequent validation experiments affirm the system's effectiveness in enhancing driving safety. In conclusion, the integration of emotion-based interaction solutions holds immense potential for elevating both driving safety and the overall travel experience within intelligent driving scenarios. this innovation promises to shape the future landscape of automotive travel, offering a safer and more enjoyable journey for all.

关键词： Human machine interaction (HMI) automotive travel feature selection speech emotion recognition machine learning emotion regulation

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machine learning and Deep learning Analysis of PCG data 4

Machine Learning and Deep Learning Analysis of PCG Data

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4th international conference on Artificial Intelligence and Signal Processing

作者： Kotipalli, Khyathi Devi Gayam, Vyshnavi Chandu, Hema Poojitha Abburi, Radha Samanta, Sibendu SRM Univ Dept CSE Amaravati Andhra Pradesh India SRM Univ Dept ECE Amaravati Andhra Pradesh India SRM Univ Dept ECE & CSE Amaravati Andhra Pradesh India

ISBN: (纸本)9798350350661;9798350350654

Cardiovascular diseases are some of the most common diseases today. A new estimate from the World Heart Federation (WHF) states that the number of deaths from cardiovascular diseases (CVD) increased from 12.1 million in 1990 to 20.5 million in the year 2021. In recent years, the field of healthcare has witnessed significant advancements in technology and data analysis techniques. Congenital abnormalities, diseases caused by impaired heart rhythm, vascular occlusion, post-operation arrhythmias, heart attacks and irregularities in heart valves are some of the various cardiovascular diseases. Early recognition of them is very important for obtaining positive results in treatment. One such area of research that holds great promise is the classification of heart sounds using phonocardiography (PCG). Classification of heart sounds has become increasingly important in enhancing diagnostic precision and enhancing patient care. the integration of machine and deep learning into medical diagnostics has emerged as a transformative avenue. machine and Deep learning techniques offer the potential to automate and increase the accuracy of cardiac sound analysis providing healthcare professionals with rapid and constant diagnostics support. this research contributes to efficient cardiac diagnostics, aiding the timely detection of abnormalities and enhancing patient care. It aspires to learn about the value of heart sound classification, the utilization of phonocardiography, and the application of deep learning and machine learning methods for enhancing the accuracy of classification.

关键词： Heart Sound classification machine learning Deep learning Phonocardiography Cardiovascular Diseases MFCCs GFCCs

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ISM 2022 - 4th international conference on Industry 4.0 and Smart Manufacturing

ISM 2022 - 4th International Conference on Industry 4.0 and ...

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4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

the proceedings contain 199 papers. the topics discussed include: infrastructure network support and leapfrogging Africa to Industry 4.0: the case of Tanzania;comparison of energy-use efficiency for lettuce plantation under nutrient film technique and deep-water culture hydroponic systems;an edge-cloud based reference architecture to support cognitive solutions in process industry;logistics 4.0 in intermodal freight transport;analysis of sustainable concrete obtained from the by-products of an industrial process and recycled aggregates from construction and demolition waste;intelligent concrete surface cracks detection using computer vision, pattern recognition, and artificial neural networks;spatial change recognition model using artificial intelligence to remote sensing;smart trip prediction model for metro traffic control using data mining techniques;encryption and generation of images for privacy-preserving machine learning in smart manufacturing;and using analytical and data-driven methods to develop a soft-sensor for flow rate monitoring in tube extrusion.

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the Effect of a Weighted Mean Pooling on the Performance of Convolution Neural Network 4th

The Effect of a Weighted Mean Pooling on the Performance of ...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Shadoul, Inas Hossen, Abdulnasir Mesbah, Mostefa Al-Hmouz, Rami Sultan Qaboos Univ Dept Elect Engn Coll Engn Al Khoud Oman

ISBN: (纸本)9783031821493;9783031821509

Convolutional Neural Networks (CNNs), a deep learning application, are powerful tools particularly suited for image processing and classification applications. Pooling is a major component of CNNs and significantly influences learning. In this step, data is reduced in size through a specific algorithm or technique, resulting in the reduction of the computational load on the layers. the various pooling techniques in CNNs have specific uses, features, and effects, some desirable and others counterproductive to the training goal. Using one pooling technique can produce results that other techniques cannot. Some cases can benefit differently from different pooling techniques. this raises the question of whether combining these pooling techniques could achieve a collective positive impact, potentially leading to performance gains beyond those achievable by individual techniques used separately. A control parameter is added to optimize the selection of the pooling method or could be a weighted combination of more than one method. the results show that the presented method guarantees the same performance as a single pooling layer at least and could be improved when weighted pooling layers are involved in some datasets.

关键词： machine learning Convolution Neural Network (CNN) Pooling Mixed pooling Image classification

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Advanced Wheat Disease Detection: Utilizing an Integrated CNN-SVM Model for Leaf Image Classification 4

Advanced Wheat Disease Detection: Utilizing an Integrated CN...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Kumar, Ravi Ranjan Rana, Gautam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331540364

Artificial Intelligence (AI) and machine learning (ML) has significantly advanced gender recognition technology, which automates the identification of a person's gender from various forms of data. this study explores the development and evaluation of a gender recognizer system leveraging the EfficientNet B0 architecture and transfer learning. the model processes input images through several steps, including dataset preprocessing, feature extraction, and classification. the effectiveness of the model is demonstrated through performance metrics such as precision, recall, F1 score, and accuracy. the results indicate a high accuracy rate of 93%, with precision and recall scores showcasing the model's robust and reliable performance in predicting gender. Additionally, the ethical considerations, including privacy concerns and potential biases in gender recognition algorithms, are discussed, emphasizing the need for fairness and transparency in the deployment of such technologies. © 2024 IEEE.

关键词： Transfer learning

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the Effective Detection of Alzheimer in the System via ML Algorithm 4

The Effective Detection of Alzheimer in the System via ML Al...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Kanna, R. Kishore Joshi, Kapil Chhabra, Gunjan Pacholi, Ambali Nimasajai, X.S. Blessing Menezes, Flosy R. Department of Biomedical Engineering Chennai India Uttaranchal University Uttaranchal Institute of Technology Department of Cse Dehradun India Department of Cse Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to Be University Uttarakhand Dehradun India Gwalior India King Khalid University College of Nursing Khamis Mushayt Saudi Arabia Bambolim India

ISBN: (纸本)9798350360165

Effective therapy for Alzheimer's disease depends on early diagnosis. Key results from four research that used machine learning to detect Alzheimer's disease during its initial level. Early research indicates that combining cognitive and neuropsychological data may enhance diagnosis. the second research interprets neuroimaging data using machine learning methods. Finding the first signs of illness is the objective here. In the third research, risk variables for Alzheimer's defect conditions are detected using machine learning algorithms. they stress the need of comprehensive data for precise prediction. A novel algorithm is presented in the fourth research. the soft voting classifier method increases recognition accuracy by operating on an averaging mechanism. these papers demonstrate how machine learning may enhance approaches for diagnosing Alzheimer's disease. To enhance therapeutic results, combine everything from cognitive evaluations to multimodal data. © 2024 IEEE.

关键词： Adversarial machine learning

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Anomaly data mining method for electric power data center business system based on decision tree 4

Anomaly data mining method for electric power data center bu...

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Li, Bin Wu, Shuang Yu, Ye Zhao, Zhongying Liu, Siyao Li, Peng State Grid Ningxia Electric Power Co. LTD. Information and Communication Company Ningxia Yinchuan750001 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Most of the current frameworks for outlier data mining are one-way structures, and the efficiency of data mining is low, which leads to the increase of data mining redundancy. therefore, this paper proposes the design and analysis of outlier data mining methods for power data middle office business systems based on decision trees. According to the actual mining requirements and standards, the initial outlier data sources are collected, and the multi-level method is adopted to improve the efficiency of data mining, and the multi-level power data system data mining framework is designed. On this basis, the decision tree outlier data mining model is constructed, and the unit grid integration is used to realize outlier data mining. the final test results show that: for the selected five data mining test cycles, the final data mining redundancy is better held 20%, which indicates that the comprehensive decision tree, the designed G power data middle office business system outlier data mining effect has been significantly improved, targeted, mining processing effect is better, and has practical application value. © 2024 SPIE.

关键词： Decision trees

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Hybrid data mining Based on Student Grade Clustering for Major Concentration Classification (Case Study: University Xyz in Serang, Banten) 4

Hybrid Data Mining Based on Student Grade Clustering for Maj...

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

作者： Atiroh, Mia Miskiatul Abdurrahman, Lukman Pratiwi, Oktariani Nurul School of Industrial and System Telkom University Information System Departement Bandung Indonesia

ISBN: (纸本)9798331506490

the development of data mining is currently very much helping decision makers to determine the right decision for the problem at hand. data mining is a technique used to analyze data by combining statistics, mathematics, and machine learning. In hybrid data mining, an important influencing factor is the process of generating different classifiers to form a general model. this study proposes to combine two data mining methods, clustering and classification. thiscombination of data mining methods is called hybrid data mining. By utilizing the technique of grouping data based on similarities and *** errors can be minimized, thereby improving the accuracy of the classification model. Using hybrid data mining methods, where the is data clustered using k-medoid model and then classified, shows that 3 out of 5 models can be effectively applied. Based on the student learning data used as the test data, Hybrid data mining methods demonstrate higher accuracy rates, such as an increase from 74% to 92% for Naive Bayes, 86% to88% for Support Vector machine, and 78% to 84% for K-Nearest Neighbor. so that concentration selection using hybrid data mining methods is more accurate. © 2024 IEEE.

关键词： data accuracy

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Enhancing OCR Model Training with Synthetic data: A Case Study with SROIE dataset 4

Enhancing OCR Model Training with Synthetic Data: A Case Stu...

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4th IEEE international conference on Smart Information Systems and Technologies, SIST 2024

作者： Yashchuk, Yuriy Yurchenko, Yuliana Yurchenko, Nataliia Andrashko, Yurii Ivan Franko National University of Lviv Lviv Ukraine Uzhhorod National University Uzhhorod Ukraine

ISBN: (纸本)9798350374865

this paper investigates the impact of synthetic data on the training effectiveness of optical character recognition (OCR) models. With the rapid advancement in machine learning, the availability of diverse and extensive datasets has become crucial for developing robust OCR systems. However, the scarcity of annotated real-world data poses significant challenges. this study focuses on performing an OCR task using the SROIE dataset, which presents a real-world challenge due to prevalent issues such as paper and ink corruption, inconsistent lighting conditions, variable fonts, colors, background noise, and imaging distortions. We introduce an approach to generating synthetic data, aiming to mimic those peculiarities. By conducting thorough experiments using the SROIE dataset and a custom synthetic training dataset, we demonstrate substantial improvements in model performance, thereby underscoring the potential of synthetic data in OCR applications. © 2024 IEEE.

关键词： Adversarial machine learning

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