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检索条件"主题词=Classification Algorithms"

共 26406 条记录，以下是4781-4790 订阅

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Integration of Deep Learning algorithms for Breast Cancer Detection using MRI Images with an STM32 Board

Integration of Deep Learning Algorithms for Breast Cancer De...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： R Karthikamani S Sukitha V Swetha C Yuvashree Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Breast cancer patient prognosis and treatment effectiveness are greatly enhanced by early identification and categorization. This research project focuses on analysing magnetic resonance imaging (MRI) images and conducting an extensive comparative investigation of five distinct deep learning algorithms to enhance the accuracy of breast cancer diagnosis. The goal is to evaluate the effectiveness and efficacy of deep learning models in correctly classifying instances of breast cancer. Different methods for identifying breast cancer using MRI, utilizing the CNN, LSTM, VGG16, and Autoencoder algorithms, are compared based on metrics such as accuracy, error rate, Classifier Success Index (CSI), F1score, Memory usage, and Execution Time. These metrics help understand their performance. In the comparative study involving five neural network architectures like CNN,LSTM,MobileNetV1,Auto-Encoder and VGG16, the Convolutional Neural Network(CNN) achieved the highest accuracy across the *** to constraints within the Edge Impulse platform,which supports certain architectures like ***,MobileNetV1 was deployed for practical *** MobileNetvV1,suitable for the STBL475EIOT01A2 Board,achieving an accuracy of 90.57% and 87.50% on the system(Intel Core i3).In conclusion, a comparison was made on execution time and memory usage between System and board.

关键词： Deep learning Measurement Accuracy Magnetic resonance imaging Neural networks Breast cancer Resonance classification algorithms Convolutional neural networks Prognostics and health management

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Industrial Scene Area Positioning Technology Based on LED Endogenous Identity Characteristics

Industrial Scene Area Positioning Technology Based on LED En...

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yanyu Zhang Bin Ba Shen Liu PLA Strategic Support Force Information Engineering University School of Information Systems Engineering Zhengzhou Shenzhen University School of Physics and Optoelectronic Engineering Shenzhen

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Due to the unique advantages of visible light positioning (VLP), its application in the industrial scene has received extensive attention. However, the implementation of VLP usually requires complex equipment and huge amount of computation, which is a huge challenge for the industrial scene with many production factors. Therefore, this paper proposes an industrial scene area positioning technology based on LED endogenous identification characteristics, which uses the same set of visible light equipment to realize communication and positioning, effectively avoiding the problems faced by current VLP applications in industrial production. The principle of this technology is to identify the LED of the transmitted signal according to the signal waveform received by the mobile terminal, and then use the corresponding position information of the LED to achieve positioning. To improve the accuracy of LED recognition, we propose a combined network of wavelet scattering and long short-term memory (WLS-LSTM) to extract signal waveform features and classify. Otherwise, we verify the classification accuracy of the WLS-LSTM through experiments. The experimental results show that the trained WLS-LSTM network model has a classification accuracy of 98.5% for six different models of LEDs, and the lowest classification accuracy of 90.2% in several different areas under each model of LED, showing good generalization. Finally, through comparison, it is found that the classification accuracy of WLS-LSTM is significantly higher than that of other common classification algorithms.

关键词： Seminars Accuracy Scattering Production Light emitting diodes Feature extraction classification algorithms

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Quality classification and Segmentation of Sugarcane Billets Using Machine Vision

Quality Classification and Segmentation of Sugarcane Billets...

引用

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

Machine learning is widely used in agriculture to optimize practices such as planting, crop detection, and harvesting. The sugar industry is a major contributor to the global economy, valuable both as a food source and as a sustainable crop with useful byproducts. This paper presents three machine vision algorithms capable of performing quality classification and segmentation of raw sugarcane billets, developing a proof-of-concept for implementation at our industry partner's mill in NSW. Such a system has the potential to improve quality and reduce costs associated with an essential yet labor-intensive, inefficient, and unreliable process. Two recent iterations of the popular You Only Look Once (YOLO) algorithm, YOLOR and YOLOX, are trained for classification, with the state-of-the-art Mask R-CNN network used for segmentation. The best performing classification model, YOLOX, achieves a classification mAP 50:95 of 90.1% across 7 classes in real time, with an average inference speed of 19.36 ms per image. Segmentation accuracy of AP 50 of 70.8% and AR 50-95 of 83.5% was achieved using the Mask CNN-R network.

关键词： Billets Image segmentation Visualization Crops Object segmentation Machine learning Sugar industry Real-time systems classification algorithms Data mining

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An Improved Weighted KNN Algorithm About Text classification Based on Spark Framework

An Improved Weighted KNN Algorithm About Text Classification...

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International Conference on Information, Communication and Networks (ICICN)

作者： Tianming Yang Shaobo Du College of Computer & Information Engineering Guizhou University of Commerce Guiyang China

ISBN: (纸本)9781665490832

K-nearest neighbor classification algorithm can quickly deal with the classification problem in this paper, but when calculating the similarity, it will assign the same weight to all distances, and does not pay attention to the impact of small distance on classification accuracy. At the same time, the k-nearest neighbor classification algorithm will be affected by the number of samples and dimensions, which will affect the efficiency of the classification algorithm. Therefore, an improved weighted KNN classification algorithm based on spark framework is proposed, which can improve the operation efficiency of the algorithm by cutting and reducing the dimension of sample data. Experimental results show that the algorithm has better accuracy and speedup ratio than the parallel algorithm based on Hadoop platform, and can process large-scale text data quickly and accurately.

关键词： Dimensionality reduction Text categorization classification algorithms Sparks Parallel algorithms

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Work-in-Progress: Utilizing Probabilistic Analysis to Fine-Tune Optimal IDK Cascades

Work-in-Progress: Utilizing Probabilistic Analysis to Fine-T...

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Real-Time Systems Symposium (RTSS)

作者： Anh-Vu Nguyen Albert M. K. Cheng Thomas Carroll Cy-Fair High School Cypress TX USA Department of Computer Science University of Houston Houston TX USA

ISBN: (数字)9798331540265

ISBN: (纸本)9798331540272

In an effort to reduce the runtime of classification algorithms, IDK (I Don’t Know) cascades have been presented as an alternative to current classification models. These structures comprise of a “cascade” of classifiers that only categorizes an input if a classifier outputs a confidence level that exceeds a predetermined threshold. If it does not, it outputs the class “I don’t know” and moves on to the next classifier in the cascade. However, these IDK cascades often reach their worst-case execution time, where classification is only completed by the final classifier. This paper aims to improve the static structure employed by these cascades, deploying a dynamic IDK framework that skips certain classifiers upon meeting specific conditions. A probabilistic analysis run on a previously validated optimal IDK cascade helped us identify these specific conditions, using the outputted confidence level of the first classifier to dictate if a cascade should run its middle classifiers. Ultimately, we generated a dynamic IDK cascade that ran up to 17% faster than its static counterpart.

关键词： Fault tolerance Analytical models Runtime Correlation Heuristic algorithms Fault tolerant systems Probabilistic logic Robustness Real-time systems classification algorithms

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Effectiveness of TLBO, SCA and WaOA for Epileptic Seizer Detection

Effectiveness of TLBO, SCA and WaOA for Epileptic Seizer Det...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Sumant Kumar Mohapatra Puspanjali Mallik K. P. Swain Alok Kumar Agarwal Rajat Bhardwaj Rajit Verma Department of ETC Trident Academy of Technology Bhubaneswar India Department of CSE Siksha ‘O' Anusandhan University Bhubaneswar India Dept.of ETC Trident Academy of Technology Bhubaneswar India School of Engineering and Technology Chitkara University Himachal Pradesh Dept. of Engineering and Technology ASET Amity University Bengaluru MM Institute of Management Maharishi Marksandeshwar (Deemed to be University) Mullana-Ambala Haryana India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Epilepsy is a brain-related disease caused due to imbalance of electricity flow in the brain. During the past two decades, researchers and neurologists have been trying their best to devise powerful EEG (electroencephalogram) devices. In the present scenario, metaheuristic optimization algorithms are integrated with machine learning algorithms to use in the seizure detection symptoms. This research studies three metaheuristic algorithms named teaching learning-based optimization algorithm (LBO), sine cosine algorithm (CSA), and Walrus Optimization algorithm (WaOA) with Wavelet Packet transform as the feature extraction technique, Analysis of Variance (ANOVA) as the feature selection technique and Least square SVM (LSSVM) as the classifier and concludes Walrus Optimization algorithm as the best among the three algorithms having average accuracy, sensitivity, specificity and area under curve as 98.52, 97.54, 93.88, and 0.98. To continue research, it uses the Bonn University EEG dataset. In the future, this result can be tested with other popularly used datasets and also integrated with the Internet of Medical Things (IoMT).

关键词： Support vector machines Machine learning algorithms Sensitivity Metaheuristics Feature extraction Wavelet analysis Electroencephalography Wavelet packets classification algorithms Analysis of variance

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Improving Availability and Scalability for RDMA Load Balancing with In-network Reordering

Improving Availability and Scalability for RDMA Load Balanci...

引用

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Jinbin Hu Ruiqian Li Shuying Rao Jin Wang Changsha University of Science and Technology Changsha China Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Remote Direct Memory Access (RDMA) is widely deployed in datacenter networks (DCNs) due to its ultra-low latency, high throughput, and low CPU overhead. Since RDMA is sensitive to out-of-order packets, the previous load balancing schemes designed based on TCP do not work well in RDMA networks. Recently proposed load balancing schemes focus on solving packet reordering within the network. However, the existing solutions cannot be extended in practice because the required queues far exceed common switch capabilities. In this paper, we propose a scalable and efficient load balancing called SELB to improve availability and scalability. SELB employs a clustering algorithm to categorize equal-cost paths and then reroutes traffic to the same cluster parallel paths to reduce the degree of out-of-order and improve queue utilization. The NS-3 simulation results demonstrate that SELB reduces the average flow completion time (FCT) and the 99th percentile FCT by up to 33% and 21%, respectively, compared to the state-of-the-art load balancing schemes.

关键词： Out of order Distributed processing Scalability Simulation Clustering algorithms Switches Load management Throughput classification algorithms

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Prediction of Technical Object State In Conditions of Unbalanced Classes on the Basis of Machine Learning Models

Prediction of Technical Object State In Conditions of Unbala...

引用

International Russian Automation Conference (RusAutoCon)

作者： Yuliya Kuvayskova Anastasiya Izyurova Olga Malenova Department of applied mathematics and informatics Ulyanovsk State Technical University Ulyanovsk Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

This article describes the method of predicting the state of a technical object in conditions of unbalanced classes in order to provide timely warning about the violation of the objects operability and the possibility of an emergency situation on the basis of machine learning models. The method uses random forest and support vector algorithms as basic machine learning models. However, when applying machine learning models to predict critical situations in the operation of an object, there is often a problem of class imbalance in the source data, i.e., a small amount of information about the emergency state of the object. In such a case, the accuracy of predicting the state of the object is significantly reduced. To improve prediction accuracy, it is necessary to build the model in such a way as to minimize the number of errors in classifying critical situations in the operation of the object under conditions of class imbalance. To solve this problem, the proposed method uses sampling algorithms: the algorithm using Tomek links and the ADASYN algorithm. F- score and AUC ROC criteria are used to evaluate the quality of prediction. The study of the effectiveness of the method of predicting the state of a technical object in conditions of unbalanced classes is carried out on the example of assessing the state of a hydraulic system pump. It was shown that the application of the proposed method improved the value of quality metrics by 3.5%-21% compared to models without data balancing.

关键词： Training Measurement Machine learning algorithms Accuracy Predictive models Prediction algorithms Data models Vectors classification algorithms Random forests

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An Anti-electricity Theft Prediction Technique Based on Large-scale Electric Power Data

An Anti-electricity Theft Prediction Technique Based on Larg...

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Clean Energy and Electric Power Engineering (ICCEPE), International Conference on

作者： Heyang Sun Feng Sun Bin Zhang Yuhang Yan State Grid Liaoning Electric Power Research Institute Shenyang Liaoning Province China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

This research suggests a new approach to address the issue of low accuracy in conventional anti-theft prediction algorithms. To increase prediction accuracy, a constraint condition of line loss rate growth rate is first added and a sample of electricity theft data is built by using anomaly rules. Subsequently, the method utilizes four machine learning classification algorithms to establish predictive models for the datasets of voltage, current, and power factor, respectively. It is possible to predict potential future electricity theft behavior by learning patterns and features of historical data. This technique generates a list of customers suspected of electricity theft by combining users with abnormal data output with users with abnormal line loss. Taking into account a wide range of anomalous circumstances, this technique can identify customers suspected of electricity theft more precisely. The trial results show that this strategy works well in terms of prediction accuracy, particularly when it comes to effectively and practically detecting people who may be stealing electricity. This indicates that the method has great potential for practical application and can provide reliable anti-theft prediction methods for the power industry.

关键词： Electric potential Reactive power Accuracy Electricity Voltage Predictive models Prediction algorithms Data models classification algorithms Reliability

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3D EKF-SLAM Using Cylindrical and Planar Features With Multilevel Mapping Representation

3D EKF-SLAM Using Cylindrical and Planar Features With Multi...

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Annual IEEE Systems Conference

作者： Leonardo Mariga Cairo Lúcio Nascimento Sérgio Ronaldo Barros Dos Santos Division of Electronic Engineering Instituto Tecnológico de Aeronáutica SP Brazil Institute of Science and Technology Universidade Federal de São Paulo SP Brazil

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

This work presents an expansion of the traditional landmark-based EKF-SLAM for 3D indoor environments. It uses a mobile robot with an RGBD camera to decompose a scene and update its 3D location. A point-cloud map is reconstructed by initially simplifying the scene into cylinders and planes. Then, the captured points are segmented and associated to their specific EKF feature. This enables the output of a map using different levels of abstraction, which is helpful when working in applications with limited memory requirements. In our feature extraction method, the RANSAC algorithm is used for planar segmentation. The non-planar points are clustered using DBSCAN and parameterized as cylinders after passing through validation gates. The classical EKF framework propagates and updates the features’ states in each step. This also triggers the point-cloud growth and increments the point-cloud of the observed feature. Our simulation results show that this method successfully reconstructs maps, reducing the IAE criteria of trajectory error to 15% of that compared to the odometry. Finally, we successfully validate our proposal in a real-world environment using a ground robot with an RGBD Intel Realsense camera.

关键词： Point cloud compression Three-dimensional displays Simultaneous localization and mapping Simulation Robot vision systems Cameras classification algorithms

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