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检索条件"主题词=Machine Learning Algorithms"

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Research on Automatic Scoring algorithms for English Essays Based on machine learning

Research on Automatic Scoring Algorithms for English Essays ...

引用

Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： You Chen Shuai Wu College of Foreign Studies Guangdong University of Science and Technology Dongguan City China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

With the rise of machine learning and natural language processing, automatic scoring of English compositions has received more robust technical support. However, current research still faces several challenging issues, such as article structure and coherence, originality, and expressiveness. Due to challenges involving subjectivity, diversity, and cultural differences, accurate evaluation remains a difficult problem for automatic scoring. To address these issues, this paper employs machine learning algorithms, including Random Forest, Gradient Boosting Decision Tree, Extreme Gradient Boosting, Light Gradient Boosting machine, Support Vector Regression, and K-Nearest Neighbor, to learn the features and patterns of compositions from a large set of training data, thereby achieving automatic scoring. The model's predictive results are evaluated based on Mean Squared Error, Root Mean Square Error, and Mean Absolute Error. Lastly, the interpretation and feedback of the scoring results are discussed, along with how to derive scoring results from the model's output and provide targeted suggestions and improvements as needed.

关键词： Support vector machines machine learning algorithms Accuracy Training data Predictive models Boosting Feature extraction Root mean square Standards Random forests

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A Comparative Study of machine learning and Deep learning algorithms for Early Detection of Brain Tumours from MRI Images

A Comparative Study of Machine Learning and Deep Learning Al...

引用

Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： C. Puvanadevi T. Menakadevi Adhiyamaan College of Engineering (Autonomous) Hosur India Dept of Electronics and Commn Engg Adhiyamaan College of Engineering (Autonomous) Hosur India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Detection of brain tumours at early stages is critical for timely intervention and improved patient outcomes. Magnetic Resonance Imaging (MRI) techniques in medical imaging plays an essential role in facilitating the detection and diagnosis of brain tumours. Over the years, advancements in machine learning and deep learning algorithms have significantly enhanced the accuracy and efficiency of tumour detection from MRI images. However, there remains a need for a comprehensive comparative study to evaluate the performance of these algorithms. The current research work presents a comprehensive comparative study aimed at evaluating the efficacy of such algorithms for early detection of brain tumours from MRI images. A novel algorithm, DT-CNN, is proposed, which combines decision trees (DT) with convolutional neural networks (CNN) to leverage the strengths of both techniques. The study encompasses simulation analyses, employing suitable metrics to assess the performance of DT-CNN alongside existing algorithms. Various simulation metrics such as precision, recall, accuracy and $F 1$-score are applied for rigorous evaluation. Results demonstrate that DT-CNN exhibits promising performance in terms of sensitivity and overall classification accuracy when compared to established algorithms. Moreover, DT-CNN demonstrates robustness in handling complex MRI image data, showcasing its potential as an effective tool for early detection of brain tumours. The study affords valued visions into the strengths and limitations of ML and DL algorithms in the context of medical imaging, facilitating informed decision-making for healthcare professionals.

关键词： Deep learning machine learning algorithms Accuracy Magnetic resonance imaging Decision making Classification algorithms Convolutional neural networks

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Assessing Basic Emotion via machine learning: Comparative Analysis of Number of Basic Emotions and algorithms

Assessing Basic Emotion via Machine Learning: Comparative An...

引用

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

作者： Caryn Vowles Mackenzie Collins T. Claire Davies Mechanical and Materials Engineering Queen’s University Kingston Canada

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper explores the use of machine learning (ML) methods to identify "clusters" of basic emotions based on pleasure, arousal, and dominance (PAD). The data was obtained from the Dataset for Emotion Analysis using Physiological Signals (DEAP), data collected within the Building and Designing Assistive Technology (BDAT) Lab using the International Affective Picture System (IAPS), and the scores of PAD from the IAPS. The objective is to develop an algorithm that maps a PAD score to clusters that express emotions, e.g., sadness or happiness. The elbow method was used to determine the optimal number of clusters (4-8), and nine different ML algorithms were compared. Decision Trees, polynomial support vector machines (SVMs) and linear SVMs provided accurate results. The Decision Tree demonstrated efficiency, during both testing and validation, in identifying the same clusters when analyzing both the DEAP and IAPS datasets. The dataset included limited data for each emotion creating the possibility of overfitting. However, when evaluating the results relative to previous research, the results added to the understanding of the nuances of emotion self-reporting and modelling.

关键词： Support vector machines Vocabulary Accuracy machine learning algorithms Clustering algorithms Data models Polynomials Decision trees Elbow Testing

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machine learning-Based mmWave MIMO Beam Tracking in V2I Scenarios: algorithms and Datasets

Machine Learning-Based mmWave MIMO Beam Tracking in V2I Scen...

引用

IEEE Latin-American Conference on Communications, LATINCOM

作者： Ailton Oliveira Daniel Suzuki Sávio Bastos Ilan Correa Aldebaro Klautau LASSE - Telecommunications Automation and Electronics Research and Development Center Belém PA Brazil

ISBN: (数字)9798331521110

ISBN: (纸本)9798331521127

This work investigates the use of machine learning applied to the beam tracking problem in 5G networks and beyond. The goal is to decrease the overhead associated to MIMO millimeter wave beamforming. In comparison to beam selection (also called initial beam acquisition), ML-based beam tracking is less investigated in the literature due to factors such as the lack of comprehensive datasets. One of the contributions of this work is a new public multimodal dataset, which includes images, LIDAR information and GNSS positioning, enabling the evaluation of new data fusion algorithms applied to wireless communications. The work also contributes with an evaluation of the performance of beam tracking algorithms, and associated methodology. When considering as inputs the LIDAR data, the coordinates and the information from previously selected beams, the proposed deep neural network based on ResNet and using LSTM layers, significantly outperformed the other beam tracking models.

关键词： Wireless communication Deep learning Global navigation satellite system machine learning algorithms Laser radar Millimeter wave technology Data integration Data models Millimeter wave communication Long short term memory

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Indian Banking Precision Marketing: A Comparative Analysis of machine learning Customer Segmentation algorithms

Indian Banking Precision Marketing: A Comparative Analysis o...

引用

Cyber Resilience (ICCR), International Conference on

作者： Gouher Ahmed Amer Ahmed Muhib Ahmed Jeya Latha Pranav Kumar School of Business Skyline University College Sharjah UAE Computer Science Birla Institute of Technology and Science Pilani-Dubai UAE Visiting Faculty Amity University Dubai Higher College of Technology Ras Al Khaimah UAE

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Precision marketing is a crucial technique in the rapidly changing Indian banking industry, as it helps improve client engagement and optimize marketing efforts. This study explores the use of sophisticated machine-learning algorithms to perform complex client segmentation, which enhances personalized marketing strategies to a higher level. This study specifically examines the application and comparative analysis of two important techniques: Recency, Frequency, Monetary value (RFM) combined with K-means clustering, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). This study rigorously assesses the efficacy of these techniques using actual transactional data from a prominent private bank in India. These findings have implications that go beyond the realm of finance, providing significant insights for professionals, scholars, and government officials. This research highlights the significant impact of modern analytical techniques on redefining customer engagement tactics in the ever-changing Indian banking market.

关键词： Clustering algorithms machine learning algorithms Computational modeling machine learning Data models Elbow Banking

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Improvement of learning Ability and Prediction Accuracy of Intelligent Software Through machine learning and Deep learning algorithms

Improvement of Learning Ability and Prediction Accuracy of I...

引用

International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Xue Yu Harbin Institute of Information Technology Harbin China

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

With the increasing demand for software, the learning ability and prediction accuracy of software can no longer meet people's needs, but there is currently no good solution. To address this issue, this study utilizes machine learning and deep learning algorithms to improve these issues. A literature review method is adopted to investigate and summarize the research on machine learning and deep learning algorithms in improving the learning ability and prediction accuracy of intelligent software. The learning ability of software is explored by collecting relevant literature, analyzing and comparing the characteristics and application scenarios of different algorithms, and a deep comparison is conducted on the prediction accuracy and learning ability of the software through experiments. The research results indicate that the prediction accuracy of the algorithm in this article is between 91% and 99%, and machine learning and deep learning algorithms have significant effects on improving the learning ability and prediction accuracy of intelligent software.

关键词： Deep learning machine learning algorithms Accuracy Software algorithms Predictive models Prediction algorithms Feature extraction Software Data mining Systematic literature review

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Condition-Based Monitoring for Failure Detection and Cause Identification in Wafer Transfer Robot Using machine learning and Explainable Artificial Intelligence algorithms

Condition-Based Monitoring for Failure Detection and Cause I...

引用

IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Jeong Eun Jeon Sang Jeen Hong Seung-Soo Han Dept. of Semiconductor Engineering Myongji University Yong-in South Korea Dept. of Information and Communication Engineering Myongji University Yong-in South Korea

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

This study introduces a method for real-time prediction of failures in Wafer Transfer Robot (WTR), which is critical in semiconductor manufacturing. Using data from acceleration sensors and fast Fourier transformation (FFT) for anomaly detection, the approach involves modeling failure data for high-risk components like bearing motor, ball screw, timing belt, robot hand, and end effector. A deep neural network (DNN) differentiates between normal and failure states, and a random forest (RF) algorithm classifies the failure components. Additionally, the Shapley additive explanations (SHAP) algorithm pinpoints the frequency bands liked to specific failure causes, enhancing the identification of failure causes in WTR.

关键词： Radio frequency machine learning algorithms Additives Artificial neural networks Prediction algorithms Robot sensing systems Classification algorithms Timing Monitoring Failure state

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Recommendation algorithms for Rural E-Commerce Business on Basis of Big Data and machine learning Technology

Recommendation Algorithms for Rural E-Commerce Business on B...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Ying Wang Jilin Agricultural Science and Technology University Jilin Jilin China

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The purpose of this article is to explore the application of LSTM (Long Short-Term Memory) algorithm in rural e-commerce business recommendation algorithms. This article analyzes how LSTM algorithm technology overcomes the limitations of traditional recommendation systems in processing large-scale data, providing personalized recommendations, adapting to dynamic environments, and integrating socio-economic factors. This article analyzes the shortcomings of existing research in depth and proposes a new recommendation system framework based on LSTM algorithm to improve recommendation efficiency, enhance user satisfaction, and promote the healthy development of rural e-commerce. This article explored the application of recommendation algorithms in rural e-commerce business through three experiments based on LSTM algorithm technology. In the first benchmark performance evaluation experiment, the experimental results showed that the LSTM algorithm achieved an accuracy of 75%, which was higher than 65% of the traditional CF (Collaborative Filtering) algorithm. The comparison experiment of recommendation results showed that the diversity of LSTM algorithm was 0.72 and CF algorithm was 0.61. In the final user satisfaction survey experiment, it was found that the average satisfaction score of the products recommended by the LSTM algorithm was 4.2 points, higher than the 3.85 points of the CF algorithm. The above data results indicate that the LSTM algorithm has better performance in rural e-commerce business, which can provide important references for improving user experience and promoting business development.

关键词： Surveys Performance evaluation machine learning algorithms Heuristic algorithms User experience Stability analysis Electronic commerce Socioeconomics Long short term memory Recommender systems

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Systematic Review on Frameworks for Intrusion Detection using machine learning and Deep learning algorithms

Systematic Review on Frameworks for Intrusion Detection usin...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Y R Bhavyashree M K Kavyashree K R Amrutha Department of ECE JSS Science and Technology University Mysuru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

These days, information technology-driven businesses and companies are primarily concerned with cybersecurity challenges and their increasing complexity. Computer systems are still quite susceptible to several kinds of assaults, even with the introduction of multiple intrusion detection systems to combat zero-day cybersecurity threats. This sophisticated cyberattack has led to severe revenue losses in previous years due to several system breakdowns and service disruptions and irreversible harm to one’s reputation. This research paper provides an extensive, methodical analysis of cyberattacks. In addition to making thorough comparisons between the papers published by reputable venues in this field from a variety of angles. To greatly increase business sustainability, private corporations, governments, and enterprises can greatly benefit from implementing the survey paper’s results and major conclusions in their local or worldwide operations. By conducting a literature review, providing preliminary details on the various intrusion detection algorithms, and evaluating their performance while considering various parameters, this study strives at investigating the machine learning and deep learning-based strategies for intrusion detection systems.

关键词： Surveys machine learning algorithms Electric breakdown Multimedia systems Government Intrusion detection Sustainable development Information technology Cyberattack Business

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machine learning-Based Ship Energy Efficiency algorithms

Machine Learning-Based Ship Energy Efficiency Algorithms

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Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Defu Zhang Mengqin Chen Wei Zhu Hongbin Lv Maritime College Tianjin University of Technology Tianjin China Tianjin University of Technology Paper Union and other carbon ship technology research centers Tianjin China Yingkou AtoN Division Northern Navigation Service Center Yingkou Liaoning China CNOOC Shipping (Tianjin) Co. Ltd Tianjin China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

This paper establishes fuel consumption prediction models using the Random Forest algorithm (RF) and Deep Neural Network (DNN) algorithm, with the models’ performance evaluated based on Mean Square Error (MSE) and the coefficient of determination (R 2 ). On this basis, a speed optimization model is developed using a DQN algorithm to dynamically adjust speed for minimizing fuel consumption. The evaluation of the model is conducted through the accumulation of reward values. The results show that the fuel consumption prediction model built using the RF algorithm achieves an MSE of 0.00064 and an R 2 of 0.99781. The model constructed with the DNN algorithm yields an MSE of 0.00516 and an R 2 of 0.99799. Comparatively, the RF algorithm slightly outperforms the DNN. Finally, the speed optimization model developed using the DQN algorithm learns the optimal policy through interactions with the environment to maximize the expected accumulated reward, ultimately enhancing energy efficiency by optimizing speed in the given environmental data.

关键词： machine learning algorithms Heuristic algorithms Biological system modeling Stochastic processes Artificial neural networks Forestry Predictive models Prediction algorithms Energy efficiency Fuels

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