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检索条件"主题词=prediction algorithms"

共 31027 条记录，以下是431-440 订阅

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Machine Learning Based Traffic prediction and Congestion Control algorithms in Software Defined Networks

Machine Learning Based Traffic Prediction and Congestion Con...

引用

Interactive Intelligent Systems and Techniques (IIST), International Conference on

作者： Yanying Xu Liaocheng Technician College Liaocheng China

ISBN: (数字)9798350374421

ISBN: (纸本)9798350374438

With the rise of Software Defined Networking (SDN) and the tremendous success of Machine Learning (ML) methods in classification, prediction, and control tasks, finding new traffic engineering techniques to adaptively and dynamically manage or route traffic in networks, ensuring service quality, and improving user experience quality has become a hot research topic in network research. SDN is a new type of network architecture with enormous growth potential. Its core idea is to separate forwarding and control, and concentrate intelligence on the controller for centralized control. This paper proposes an ML based traffic prediction and congestion control algorithm to address the problem of uneven load on the control plane of SDN architecture due to the complexity and variability of network traffic. Historical traffic data is collected from the SDN controller and feature extraction is performed. The extracted features are used to train the ML model and predict future traffic. Based on predicted traffic information and perceived congestion, the controller can dynamically adjust network parameters to avoid or alleviate congestion. The experimental results show that the algorithm proposed in this paper can achieve more efficient network traffic management and congestion control in SDN, thereby improving network performance and user experience.

关键词： Heuristic algorithms Computational modeling Software algorithms Telecommunication traffic Computer architecture Predictive models prediction algorithms Feature extraction User experience Software defined networking

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A Comparative Analysis of Lung Cancer prediction using Machine Learning algorithms

A Comparative Analysis of Lung Cancer Prediction using Machi...

引用

Global Conference for Advancement in Technology (GCAT)

作者： Akash Badhan Simarjit Singh Malhi Hassandeep Singh Parampreet Kaur Abhishek Parihar School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

In today’s world, with the human’s lifestyle has totally changed as compared to its ancestors which causes many types of life taking diseases. One of them is Lung cancer. The growth of cancer diseases is a big issue yet to be resolved in today’s world. The easiest way to control cancer is to predict it at its earlier stage. The aim of this paper is to examine the different algorithms of machine learning to predict the lung’s cancer and check the algorithm which offer best accuracy so that a reliable, efficient, and robust model can be designed for early detection of lung cancer which can save life of million human beings. The algorithms we have implemented in the implementation of this research are Decision Tree, KNN, and Random Forest. The parameters that we are considering for comparing the best model are Accuracy, Precision, Recall and F1 score.

关键词： Analytical models Machine learning algorithms Accuracy Lung cancer Predictive models Nearest neighbor methods prediction algorithms Classification algorithms Decision trees Random forests

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App Based Cardiovascular Disease prediction using Machine Learning algorithms

App Based Cardiovascular Disease Prediction using Machine Le...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： B M Manjula C O Dhanush G H Gurudeep M Y Likhith Department of Electronics and communication Engineering Nitte Meenakshi institute of Technology Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Study investigates machine learning (ML) algorithms for early detection of cardiovascular diseases (CVDs), given their significant global impact. Utilizing demographic data, medical history, and lab results, algorithms like Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbors are examined. An ensemble model, incorporating Random Forest, is developed for improved accuracy, initially achieving 95%. Hyperparameter optimization boosts accuracy to 99%. An Android app interface facilitates easy access to the predictive model, empowering users for CVD risk assessment. These findings underscore ML’ s potential in early CVD detection, offering advancements in predictive healthcare. This contributes to addressing CVD burdens and improving public health outcomes.

关键词： Support vector machines Machine learning algorithms Predictive models prediction algorithms Cardiovascular diseases Risk management Public healthcare

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Epileptic State prediction using Phase Space Domain and Machine Learning algorithms

Epileptic State Prediction using Phase Space Domain and Mach...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Boluwatife Faremi Yedukondala Rao Veeranki Hugo F. Posada-Quintero Biomedical Engineering University of Connecticut Storrs USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Epilepsy is a disease of the brain that causes unprovoked or reflex seizures that affects millions of individuals worldwide. Traditionally, identifying epileptic states involves assessing neuroimaging scans or brain electrical signals recorded by EEG devices. However, due to the complex nature of these signals, there are growing demands for developing predictive systems that can improve the detection of this brain condition through unseen discriminating features. This study investigates predicting and detecting epileptic states by transforming 2-dimensional EEG time series data to the Phase space domain. The angular distance and probability density function between phase vectors were computed in the new domain to extract features. Renyi and Tsallis complex features were mainly extracted to train probabilistic, discriminatory, tree, and kernel-based models. The performance of the learning algorithms was evaluated using leaveone-subject-out cross-validation. Results revealed that the probabilistic models combined with complex features from the phase domain had a 91.5% accuracy compared to other algorithms. This result indicates the efficacy of the phase space domain for detecting and predicting epilepsy states.

关键词： Machine learning algorithms Accuracy Computational modeling Epilepsy Feature extraction Brain modeling Probabilistic logic prediction algorithms Electroencephalography Vectors

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prediction and Comparative Analysis of the Stress Level of Humans by Using the Machine Learning algorithms

Prediction and Comparative Analysis of the Stress Level of H...

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： C. Sivaraj J. Angeljulie D. Hemamalini Vikram Neerugatti Narayan Panchal T.R. Mahesh Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore Tamilnadu Department of Artificial Intelligence and Data Science Arjun College of Technology Coimbatore Tamilnadu Department of Computer Science and Engineering Jain (Deemed-to-be-University) Bangalore Karnataka

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Some of the techniques that have been used include Naive Bayes, K-Means Clustering, Support Vector Machine (SVM) and k-Nearest Neighbors (KNN). To provide useful information to researchers and practitioners, the study reflects the performance, strengths and weaknesses of each algorithm using several datasets. The ability of SVM to model complex decision boundaries is evaluated. It is widely applied in high-dimensional scenario. This paper focuses on the analysis of the advantages and disadvantages of using KNN for classification purposes, such as its simplicity of use, flexibility, and the fact that the classification accuracy and the sensitivity to the selection of parameters should not be overlooked. The performance of the unsupervised K-Means Clustering algorithm in partitioning data into clusters is assessed. The method of decision trees is considered, and the question of their capability to model non-linear dependencies is raised. Finally, the performance and relevancy of the probabilistic classifier Naive Bayes for text categorization is also evaluated. This research thus offers valuable guidance to practitioners regarding the strengths and weaknesses of the proposed algorithms for categorisation, based on a detailed comparative analysis.

关键词： Support vector machines Machine learning algorithms Text categorization Clustering algorithms Nearest neighbor methods Probabilistic logic prediction algorithms Vectors Classification algorithms Bayes methods

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Optimising Random Forest Machine Learning algorithms for User VR Experience prediction Based on Iterative Local Search-Sparrow Search Algorithm

Optimising Random Forest Machine Learning Algorithms for Use...

引用

International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Xirui Tang Feiyang Li Zinan Cao Qixuan Yu Yulu Gong College of Computer Sciences Northeastern University Boston MA USA Department of Computer Science University of Illinois Urbana-Champaign Champaign IL USA Department of General Systems Studies The University of Tokyo Tokyo Japan College of Computing Georgia Institute of Technology Atlanta GA USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In this paper, an improved method for VR user experience prediction is investigated by introducing a sparrow search algorithm and a random forest algorithm improved by an iterative local search-optimised sparrow search algorithm. The study firstly conducted a statistical analysis of the data, and then trained and tested using the traditional random forest model, the random forest model improved by the sparrow search algorithm, and the random forest algorithm improved based on the iterative local search-sparrow search algorithm, respectively. The results show that the traditional random forest model has a prediction accuracy of 93% on the training set but only 73.3% on the test set, which is poor in generalisation; whereas the model improved by the sparrow search algorithm has a prediction accuracy of 94% on the test set, which is improved compared with the traditional model. What is more noteworthy is that the improved model based on the iterative local search-sparrow search algorithm achieves 100% accuracy on both the training and test sets, which is significantly better than the other two methods. These research results provide new ideas and methods for VR user experience prediction, especially the improved model based on the iterative local search-sparrow search algorithm performs well and is able to more accurately predict and classify the user's VR experience. In the future, the application of this method in other fields can be further explored, and its effectiveness can be verified through real cases to promote the development of AI technology in the field of user experience.

关键词： Training Accuracy Machine learning algorithms Predictive models prediction algorithms Iterative algorithms User experience

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A Comparative Analysis with Various Machine Learning algorithms for prediction of Liver Cirrhosis

A Comparative Analysis with Various Machine Learning Algorit...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Suman Chowdhury Dilip Kumar Das Department of Electrical and Electronic Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Mathematics International University of Business Agriculture and Technology Dhaka Bangladesh

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

This paper represents the comparative analysis for prediction of liver cirrhosis using various machine algorithms. In recent years, the liver diseases are becoming so fatal that more investigations are required to correctly diagnose the disease profile. In this regard, the machine learning algorithms provide a good platform for forecasting the liver cirrhosis diseases in the people coming hospitals with various symptoms. In this paper, confusion matrix, accuracy score, F1 score have been used as a decision tool for various machine learning algorithms including SVM(Support vector machine), Logistic regression, Random forest, KNN(K Nearest Neighbor), Gaussian Naïve Bayes. After smote re sampling the output feature, it has been observed that the Random forest has successfully predicted the liver cirrhosis with a F1 score of 0.82.

关键词： Logistic regression Machine learning algorithms Accuracy Liver Predictive models Nearest neighbor methods prediction algorithms Vectors Random forests Diseases

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prediction of Ayurveda Herbs and its Benefits using CNN and MobileNet algorithms in Deep Learning

Prediction of Ayurveda Herbs and its Benefits using CNN and ...

引用

Intelligent Technologies (CONIT), International Conference on

作者： Sambhram Ghosh M Thenmozhi Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science & Technology (SRMIST) SRM Nagar Kattankulathur Chengalpattu District Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Ayurveda has been an integral part of ancient Indian medicine and it still plays an important role in the creation of Homeopathic medicines. In this paper we have introduced a comprehensive system for detecting, grading, and classifying Ayurveda Herbs by taking images as input and analyzing those images using two Deep Learning algorithms CNN and MobileNet. The proposed system employs advanced technology to accurately identify different categories and classes of Ayurvedic Herbs, utilizing image processing and machine learning techniques for classification and gradation using Deep Learning algorithms like CNN and MobileNet. Additionally, it stores a pre-trained uses and benefits data for each and every Ayurvedic Herbs Class and displays the information based on the class of Ayurveda Herbs detected.

关键词： Deep learning Technological innovation Machine learning algorithms Refining Medical services prediction algorithms Real-time systems

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Parkinson prediction Using Voice Data and Machine Learning algorithms

Parkinson Prediction Using Voice Data and Machine Learning A...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： B. Meena Preethi Y.S Krishha C.M Nazrin N Jayaswethaashree Department of Software Systems and AIML Sri Krishna Arts and Science College Coimbatore India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Parkinson’s disease is a prevalent neurological disorder affecting a significant portion of the global population. Early diagnosis is crucial for effective intervention and management, leading to improved patient outcomes. This study evaluates Parkinson’s disease by analyzing speech data, which typically shows distinct variations in affected individuals. Apply leverage advanced machine learning techniques and artificial intelligence to develop predictive models that can efficiently differentiate between speech data from Parkinson’s patients and healthy individuals. The model is trained on an extensive dataset of audio recordings, capturing all necessary attributes for accurate diagnosis. This work employs three machine learning algorithms K-Nearest Neighbor, Random Forest Classifier, and Decision Tree Classifier to identify the most effective model. The final approach demonstrates high efficacy in detecting Parkinson’s disease, enabling early diagnosis and personalized management strategies. This study underscores the transformative potential of machine learning and AI in Parkinson’s disease detection, presenting innovative approaches to patient care and management.

关键词： Neurological diseases Green energy Machine learning algorithms Accuracy Medical services Predictive models Nearest neighbor methods prediction algorithms Random forests Diseases

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Application Research of Machine Learning algorithms in Big Data Mining and prediction

Application Research of Machine Learning Algorithms in Big D...

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Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Hao Wang School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

Student information mining plays a significant role in student management, yet existing information management methods are relatively outdated. Machine learning algorithms have the capability to predict future trends based on historical data. Whether it's sales forecasting, stock price prediction, weather forecasting, or disease spread prediction, machine learning algorithms can leverage big data for training, thereby enhancing prediction accuracy and reliability. This paper proposes the use of machine learning algorithms for clustering analysis of student information. Currently, the LSTM algorithm suffers from runtime issues. Hence, we propose improvements to the LSTM algorithm. We introduce the Grey Wolf algorithm into the existing algorithm to construct one with fast convergence speed and high prediction accuracy. Applying the improved algorithm to student information clustering, experimental results demonstrate a significant improvement in the accuracy of student information identification.

关键词： Training Machine learning algorithms Simulation Clustering algorithms Weather forecasting Big Data prediction algorithms

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