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the application of machine learning algorithms in image processing denoising 4

The application of machine learning algorithms in image proc...

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4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Lu, Di Xiong, Zhengqiang Wuhan Business University WuHan HuBei430000 China

ISBN: (纸本)9781510686106

In order to solve the problem of losing the original information of images in traditional image processing methods during denoising, this paper proposes an application method of machine learning algorithm in image processing denoising. this also means that it is necessary to continuously compare the processed image with the original image, which will increase the time required for denoising. However, deep learning techniques have played an important role in this regard. through intelligent technology, especially supervised machine learning algorithms, information images can be better processed, enabling people to observe things more clearly and take correct actions. Specifically, we can use supervised machine learning algorithms to extract noise during image denoising. this method is based on setting separation nodes for image noise at multiple points, and then using clustering theory to filter out noise, thereby completing image denoising. the experimental results show that using animal images as the test object, we can detect the noise contained in them, and by comparing the effects of the original method and the new method, we found that the new method can obtain results consistent with the original image. More importantly, without introducing image distortion, the new method controls the denoising time to 1.15 seconds, while the original method takes an average of 20.21 to 15.13 seconds. this indicates that the new method can significantly improve the efficiency of denoising and has the potential for practical applications. © 2024 SPIE.

关键词： Contrastive learning

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Cloud-Driven AI: Enhancing machine learning Performance through Distributed Computing 4

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dixit, Kunal Himani Bhardwaj, Parul Sachdeva, Ritu Manav Rachna International Institute of Research & Studies School of Computer Applications Faridabad India

ISBN: (纸本)9798331518981

the exponential data volume and complexity of machine learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. this research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application's efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. this framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5-12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. these results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today's AI applications while opening the door to new advancements in ML throughout different domains. © 2024 IEEE.

关键词： Distributed cloud

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4th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2023) 23

4th Workshop on Patent Text Mining and Semantic Technologies...

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46th international ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Krestel, Ralf Aras, Hidir Andersson, Linda Piroi, Florina Hanbury, Allan Alderucci, Dean ZBW Leibniz Informat Ctr Econ Kiel Germany Univ Kiel Kiel Germany FIZ Karlsruhe Leibniz Inst Informat Infrastruct Eggenstein Leopoldshafen Germany Artificial Researcher IT GmbH Vienna Austria TU Wien Vienna Austria Res Studios Austria Vienna Austria Carnegie Mellon Univ Ctr AI & Patent Anal Pittsburgh PA USA

ISBN: (纸本)9781450394086

Information retrieval systems for the patent domain have a long history. they can support patent experts in a variety of daily tasks: from analyzing the patent landscape to support experts in the patenting process and large-scale information extraction. Advances in machine learning and natural language processing allow to further automate tasks, such as paragraph retrieval or even patent text generation. Uncovering the potential of semantic technologies for the intellectual property (IP) industry is just getting started. Investigating the use of artificial intelligence methods for the patent domain is therefore not only of academic interest, but also highly relevant for practitioners. Compared to other domains, high quality, semi-structured, annotated data is available in large volumes (a requirement for supervised machine learning models), making training large models easier. On the other hand, domain-specific challenges arise, such as very technical language or legal requirements for patent documents. the focus of the 4th edition of this workshop will be on two-way communication between industry and academia from all areas of information retrieval in particular with the Asian community. We want to bring together novel research results and the latest systems and methods employed by practitioners in the field.

关键词： patent analysis text mining semantic technology deep learning

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4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

4th International conference on Innovative Manufacturing, Me...

引用

4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

ISBN: (纸本)9789819988181

the proceedings contain 50 papers. the special focus in this conference is on Innovative Manufacturing, Mechatronics and Materials Forum. the topics include: Effect of Crack Length, Depth, and Location on Natural Frequencies of Railway Track;imputation Analysis of Time-Series Data Using a Random Forest Algorithm;harnessing machine learning, Blockchain, and Digital Twin Technology for Advanced Robotics in Manufacturing: Challenges and Future Directions;humanizing Humanoids: An Extensive Review on the Potential of Prosthetic Robotic Arm with Integrated Monitoring System for Disabled People;intelligent Machining Systems for Robotic End-Effectors: State-of-the-Art and Toward Future Directions;artificial Neural Network Analysis in Road Crash Data: A Review on Its Potential application in Autonomous Vehicles;the Role and Impact of Robotics Integration in Precision Machining and Manufacturing: A Comprehensive Review;nanomaterial in Robotics: Bridging the Gap Between Current applications and Future Possibilities;Optimized-ELM Based on Geometric Mean Optimizer for Bearing Fault Diagnosis;detection of Fault Features in Remanufacturing of Automotive Components Using Image Processing and computer Vision Techniques;the Significance of the thoracic Spinal Multiple Segments During Different Pick-Object Approaches;Vibration Suppression of the Flexible Beam Structure Using PID Controller Tuned by Advanced Firefly Algorithm;Bearing Fault Diagnosis Based on Prominence Peak-Picking IMFs Selection and PSO-SSAE;investigation of Wheel Robot Grouser Width Parameter Effect on Robot Mobility in Soft Sand Terrain Using Sand Test Bed;automated Harmonic Signal Removal-Based Image Feature Extraction Technique: A Comparative Study Using Online Databases;Human Mental Stage Interpretation Based on the Analysis of Electroencephalogram (EEG) Signals.

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A machine learning Based Predictive Model to Diagnose Heart Failure Patients using Imbalanced Classification Problem 4

A Machine Learning Based Predictive Model to Diagnose Heart ...

引用

4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Mudassar, Muhammad Afzal, Mehtab Muhammad, Tufail Univeristy of Lahore Department of Software Engineering Lahore Pakistan Air Univeristy Islamabad Aerospace and Aviation Campus Department of Computer Science Kamra Pakistan

ISBN: (纸本)9781665463775

Heart failure (HF) is now one of the most common diseases, causing approximately seventeen million death cases every year all over the world. HF occurs due to less pumping ratio of blood by the heart that a normal human being needs to survive. In this regard, research studies have been proposed to predict the causes behind the heart failure of a patient using the 'Heart failure clinical records (HFCR's) dataset. Although, much research has been performed on this dataset, however, there is a lack of construction of a more reliable predictive model that helps to predict HF patients with better prediction results. We aimed to apply imbalance learning to handle the imbalance dataset as a very few researchers applied it. We trained the models using six ensemble and three non-ensemble classifiers with the help of multiple experiments. In the end, we performed an evaluation measure to compare our prediction results with the previous research work. Our proposed model gives a significant increase in accuracy value as well as in precision, recall, and f1-score. © 2023 IEEE.

关键词： Forecasting

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application of Data Visualization Technology in Fault Diagnosis and Maintenance of Intelligent Electromechanical Systems 4

Application of Data Visualization Technology in Fault Diagno...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Lu, Qimeng Software Engineering Institute of Guangzhou Guangzhou China Graduate University of Mongolia Ulaanbaatar Mongolia

there are problems of low efficiency and low accuracy in the fault location and maintenance process of electromechanical systems. In order to solve this problem, this article adopts a comprehensive literature analysis and case study method to conduct in-depth research on the application of data visualization technology in intelligent electromechanical systems. By collecting and analyzing relevant literature, and combining with practical cases, this article explores the specific application methods and effects of data visualization technology in fault diagnosis and maintenance. Research has shown that the diagnostic accuracy of this method is between 94% -99%, and it has good fault diagnosis ability. Data visualization technology plays an important role in the fault diagnosis and maintenance of intelligent electromechanical systems. By visualizing the real-time data and fault information of the system, maintenance personnel can intuitively understand the system status and quickly locate the root cause of the fault. © 2024 the Authors.

关键词： K-means clustering

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application research of association rule based on Apriori algorithm 4

Application research of association rule based on Apriori al...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Li, Xuemei Rosas, Maryli F. College of Electronic and Electrical Engineering Anhui Sanlian University China College of Science and Computer Studies - Graduate Studies De la Salle University -Dasmariñas City of Dasmariñas Cavite Philippines

ISBN: (纸本)9798400709449

Apriori algorithm and fp-growth are both classical algorithms of association rules. this paper collects the employment situation of graduates of a college of Electrical engineering in recent three years, and analyzes the data based on the association rules of the Apriori algorithm. Apriori filters a lot of irrelevant data sets, but this algorithm also has a disadvantage, is that every time the candidate elimination data algorithm is completed, it needs to scan all the data records, which causes the algorithm may consume a little more time. this paper provides some suggestions and references for the application of Apriori algorithm. © 2023 ACM.

关键词： Association rules

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Analysis of machine learning Algorithms in Predicting Chronic Kidney Diseases Based on Performance: A Systematic Literature Review 4

Analysis of Machine Learning Algorithms in Predicting Chroni...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Alpaullivarez, Angelique Aurielle Aurelia, Miracle Monica, Sheila Suri, Puti Andam School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Chronic Kidney Disease (CKD) has been increasing nowadays from 750 million cases in 2019 to 850 million in 2023. Lack of access and late detection are one of the reasons why CKD cases have been increasing for all these years. Various machine learning (ML) algorithms have been studied to detect CKD and this paper will discuss and compare different types of algorithms to detect CKD early before progressing to advanced stages based on its performance. Although the use of ML to predict chronic kidney disease in the medical world is still hard to find, in other literature reviews with similar topic, it is found that various ML algorithms have been developed with the goal of assisting medical professionals in predicting CKD. Highly accurate ML algorithms have also been discovered using Artificial Neural Network, Random Forest, and XGBoost, despite needing further improvement in quality to be able to be applied on a large scale. In this paper, 5 commonly used ML algorithms namely XGBoost, Random Forest, Support Vector machine (SVM), Artificial Neural Networks (ANN), and logistic regression were analyzed. Using Systematic Literature Review as this paper's methodology, this paper provides detailed explanation on how the algorithms work, implemented, and their performance were also analyzed by using 5 parameters: AUC, accuracy, precision, recall or sensitivity, and F1-Score. It was found that XGBoost achieved the highest score among all parameters except for AUC, which highest score was possessed by Artificial Neural Network. © 2023 IEEE.

关键词： Forecasting

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A Comparative Study For Predicting House Price Based on machine learning 4

A Comparative Study For Predicting House Price Based on Mach...

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4th international conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Eze, Elias Sujith, Sujith Sharif, Mhd Saeed Elmedany, Wael ACE UEL Computer Science and Digital Technologies Department London United Kingdom University of Bahrain College of Information Technology Bahrain

ISBN: (纸本)9798350369786

this study presents the development and comparison of four machine learning approaches, namely, Random Forest, Decision Tree, Linear Regression and k-Nearest Neighbours (k-NN), to perform house prices prediction based employing the Boston Housing Dataset. the approaches performance was evaluated using root mean square error as well as R2, with the aim of identifying the model that best predicts housing prices. the dataset was thoroughly analyzed for features, correlations, multicollinearity, and overfitting. Results indicate that the RF model outperformed the other models in predicting house prices, as it has ability to address non-linearity and complex interactions among variables and reduce the impact of outliers. the DT model also performed well but may have been more prone to overfitting. LR, on the other hand, may have been limited by its assumptions of linearity and independence among variables. © 2023 IEEE.

关键词： k-nearest neighbors

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Enhanced Credit Card Fraud Detection: A machine learning Approach 4

Enhanced Credit Card Fraud Detection: A Machine Learning App...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Meena, Priti Raj, Mukul Jindal, Rajni Delhi Technological University Computer Science Engineering New Delhi India

ISBN: (纸本)9798350349900

With the swift advancement in technology, credit cards are increasingly prevalent as substitutes for cash in daily transactions. this presents several opportunities for unscrupulous individuals to use credit cards fraudulently. 425,977 reports of credit card fraud were made in 2023, according to a Federal Trade Commission report. the credit card issuer ought to offer a service that shields customers from potential risks to guarantee their safety when using these cards. To address the problem of class imbalance, we resampled the dataset using the Synthetic Minority Oversampling Technique (SMOTE). the following variety of machine learning methods were applied to assess this framework using a soft voting mechanism: Combination 1 consists of KNN, Bagging, AdaBoost, and Logistic Regression, whereas Combination 2 consists of Random Forest, KNN, Bagging, and XGBoost. Furthermore, the usefulness of Logistic Regression, KNN, and AdaBoosting individually was evaluated to determine their stand-alone performance. this comparison served as a benchmark against which the ensemble models were evaluated. the models' accuracy, F1 score, and Area Under the Curve (AUC) were used to evaluate them. Additionally, the outcomes of the machine learning models combined produced better results, with a total accuracy of 99.81% for combination 1 and 99.86% for combination 2 compared to the individual predictions of the models. However, Combination 2 outperformed Combination 1 in terms of F1 score, suggesting enhanced overall recall and precision performance. © 2024 IEEE.

关键词： Random forests

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