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检索条件"主题词=Machine Learning Algorithms"

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A Comparative Study on machine learning algorithms for Knowledge Discovery

A Comparative Study on Machine Learning algorithms for Knowl...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Siddesh Sambasivam Suseela Yang Feng Kezhi Mao Nanyang Technological University Singapore Singapore IHPC A*STAR Singapore Singapore

ISBN: (纸本)9781665476881

For centuries, the process of formulating new knowledge from observations has driven scientific discoveries. With rapid advancements in machine learning, it is natural to question the possibility of automating knowledge discovery in the scientific field. A benchmark task for automated knowledge discovery is called symbolic regression. The task aims to predict a mathematical equation that best describes the observational data. The advancements in symbolic regression have significant potential to aid research in understanding unexplored systems' dynamics and governing properties. However, the combinatorial nature of the problem makes it an expensive and challenging problem to solve efficiently. Several types of symbolic regression algorithms exist, from genetic programming and sparse regression to deep generative models. However, no survey collates these prominent algorithms. Therefore, this paper aims to summarize key research works in symbolic regression and perform a comparative study to understand the strength and limitations of each method. Finally, we highlight the challenges in the current methods and future research directions in the application of machine learning in knowledge discovery.

关键词： machine learning algorithms System dynamics Heuristic algorithms Genetic programming machine learning Benchmark testing Knowledge discovery

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A Novel Framework of Network Packet Loss Detection Using Random Forest Algorithm over Support Vector machine learning algorithms to Improve Accuracy

A Novel Framework of Network Packet Loss Detection Using Ran...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： NV Midhun Sai M.S. Saravanan P. Subramanian Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India

The objective of this paper is to employ a novel framework of network packet loss detection with increased accuracy rate using Novel Random Forest classifier compared to a Support Vector machine (SVM) Classifier. Materials & Procedures: The data set used in this study utilizes the publicly available Kaggle network traffic data set and UCI machine learning repositories. The sample size of network packet loss detection with improved accuracy rate sample size was 50 (Groups 1 and 2 each had 25 participants), and the computation was performed using a G-power of 0.8, with alpha and beta values of 0.05 and 0.2 and a 95% confidence interval. A network packet loss detection with improved accuracy rate is performed by Random Forest (RF) whereas multiple samples $(\mathrm{N}=10\mathrm{A})$ SVM were a number of samples $(\mathrm{N}=10)$ Results: The Novel Random Forest classifier has 93.56% higher accuracy rates in comparison to the accuracy rate of SVM is 89.23%. There exists a statistical significance difference between two groups $(\mathrm{p}=0.0231;\mathrm{p} < 0.05)$ with confidence interval 95%. Conclusion: Novel Random Forest when compared, a classifier offers more accurate results. to SVM for network packet loss detection with improved accuracy rate.

关键词： Support vector machines Knowledge engineering machine learning algorithms Packet loss Support vector machine classification Telecommunication traffic Classification algorithms

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Application of machine learning algorithms to Path Loss Modeling: A Review

Application of Machine Learning Algorithms to Path Loss Mode...

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Information Technology for Education and Development (ITED)

作者： Abubakar Abdulkarim Nasir Faruk Emmanuel Alozie Olugbenga. A. Sowande Imam-Fulani Yusuf Olayinka Aliyu D. Usman Kayode S. Adewole Abdulkarim A. Oloyede Haruna Chiroma Salisu Garba Agbotiname Lucky Imoize Abdulwaheed Musa Lawan S. Taura Department of Electrical Engineering Ahmadu Bello University Nigeria Department of Electrical Telecommunications & Computer Engineering Kampala International University Uganda Department of Physics Sule Lamido University Nigeria Directorate of ICT Sule Lamido University Nigeria Department of Telecommunication Science University of Ilorin Nigeria Department of Electronics and Telecommunication Engineering Ahmadu Bello University Nigeria Department of Computer Science University of Ilorin Nigeria College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Computer Science Sule Lamido University Nigeria Department of Electrical and Electronics Engineering University of Lagos Nigeria Department of Electrical and Computer Engineering Kwara State University Nigeria

The demand for high-speed internet services is increasing due to emerging needs such as e-commerce, e-health, education, and other high-technology applications. Wireless communication networks have now become a necessity, especially with the introduction of the 5G networks which have the potential to provide extraordinary data rates with extremely low latency. The deployment and operation of 5G and beyond networks in built-up environments would require a complex and reliable radio propagation model that guides network engineers to achieve effective coverage estimation and appropriate base station placements. The inefficiency, and sometimes inconsistencies of deterministic and empirical path loss models necessitated the need to integrate machine learning models. Recently, different machine learning-based pathloss models have been developed to overcome drawbacks associated with conventional pathloss models due to their significant learning and prediction abilities. This paper aims to review path loss models relative to machine learning-based algorithms with a focus on models developed in the last 21 years (2000 to 2021) to study their network parameters and architectures, designs, and applicability, and proffer further research directions.

关键词： Support vector machines Wireless communication machine learning algorithms 5G mobile communication Education Web and internet services machine learning

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Investigation on machine learning algorithms to Predict High School Students Performance at Risk of Failure

Investigation on Machine Learning Algorithms to Predict High...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Fatema Almehaiza Layla Jaafar Iyad Ali Mohammad Alatoom Nabil Mahmood Hewahi MSc. in Big Data Science and Analytics College of Science University of Bahrain Department of Computer Science College of Information Technology University of Bahrain

ISBN: (纸本)9781665490597

Successful learning is dependent on the success of the learners; ensuring students’ achievement is the responsibility of the educational system. Over the years, educational institutions have utilized machine learning (ML) technology to facilitate and support decision-making in a variety of learning areas. Predicting students’ performance in the early stages of their learning journey is one of ML’s most significant contributions to educational institutions. Using ML technology, the institutes will be able to improve teaching and learning practices for students who are at risk of failing. The objective of this study is to build a model that can predict students’ performance and identify students at risk of failure. The study examines the application of three ML algorithms in predicting: Logistic Regression (LR), Decision Tree (DT) and Random Forest (RF). The study used a sample size of 647 with one dependent variable and 33 distinct variables. The study found that the two tree-based methods performed better than LR, although the precession measure out of four measures shows that LR is slightly better. Overall, in this study RF has showed to be the best among them with an accuracy of80.37%, recall of 87.98%, and F1 rate of 91.78%.

关键词： Radio frequency machine learning algorithms Education Sociology Predictive models Prediction algorithms Reproducibility of results

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Intelligent Telework Internet Cost Requirement Modeling Using Optimizable machine learning algorithms

Intelligent Telework Internet Cost Requirement Modeling Usin...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Ryan Rhay P. Vicerra Rex Paolo C. Gamara Jayne Lois G. San Juan Argel A. Bandala Ronnie Concepcion Elmer P. Dadios Pocholo James M. Loresco Jason E. Espanola Alvin Culaba Andres Philip Mayol Department of Manufacturing Engineering and Management De La Salle University Manila Philippines Department of Electronics and Computer Engineering De La Salle University Manila Philippines Department of Industrial and Systems Engineering De La Salle University Manila Philippines Electrical and Electronics Engineering Department FEU Institute of Technology Manila Philippines Department of Mechanical Engineering De La Salle University Manila Philippines

The COVID-19 pandemic has caused disruption to the economy due to the increasing infection that affects the workforce in different sectors. The Philippine government has imposed lockdowns to control the spread of infection. This urged the different sectors to implement flexible work schedules or work from home setup. A work-from-home (WFH) setup burdens both the employee and employer by installing different equipment set-ups such as WiFi-equipped laptops, computers, tablets, or smartphones. However, the internet stability in some of the areas in the Philippines is not yet reliable. In this study, an application is used collect survey information and provide an estimate of the telework internet cost requirement of a given government employee or a given government employee implementing a work-from-home set up in their respective household. This involves survey results from different respondents who are currently on a work-from-home setup and significant factors from the survey have been analyzed using machine learning (ML) algorithms. Among the machine learning algorithms used, the ensemble bagged trees model outperformed the other ML models. This work can be extended by incorporating a wider scope of datasets from different industry doing work from home set-up. In addition, in terms of education, it is also recommended to determine the WFH set up not just with the government employee and employer but to also extend this into the education side.

关键词： Schedules machine learning algorithms Costs Portable computers Pandemics Government Remote working

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A Review on Comparison of machine learning algorithms for Text Classification

A Review on Comparison of Machine Learning Algorithms for Te...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Mallika Dhingra Dharmesh Dhabliya M. K. Dubey Ankur Gupta Dhoma Harshavardhan Reddy Department of Mathematics and Statistics Manipal University Jaipur Jaipur Rajasthan India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Mumbai Maharashtra India

The majority of the data is preserved as text (about 75%), hence It is believed that text mining has a significant commercial potential. Unstructured texts continue to be the most readily available source of knowledge, despite the fact that knowledge may be accessed in many other places. Text classification that assigns documents to predetermined categories. machine learning approaches can categories texts more accurately. The goal of this work is to introduce text classification, give a description of the text classification technique, a general review of the classifiers, and a comparison of a few of the current classifiers. It is based on performance, time complexity, and other factors. On the basis of speed, accuracy, benefits, and drawbacks of existing classification methods such as Decision Tree, Naive Bayes, Support Vector machine, and k-Nearest Neighbours are compared.

关键词： Text mining machine learning algorithms Text categorization Web pages Support vector machine classification machine learning Writing

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Comparative Analysis of machine learning algorithms for classification of Alzheimer’s disease

Comparative Analysis of Machine Learning Algorithms for clas...

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Emerging Trends in Engineering and Medical Sciences (ICETEMS), International Conference on

作者： Akshara Madhu Suthanan Siddharth Rathee Anil Kumar Department of Computer Science & Engineering Manipal University Jaipur Jaipur India

With significant increase in interest in dementia and Alzheimer’s disease, a form of dementia that deteriorates the brain as well as the memory along with other important mental functions, in which brain neuron connections begin to disintegrate and die. Memory loss and confusion being the main symptoms, physicians and scientists are yet to find a concrete cure for the same. However, different strategies and medications have been found to be helpful, especially if early detection is possible. The report shows the results and analysis of detecting Alzheimer’s using machine learning models and compared them. The OASIS dataset has been applied to different machine learning models like SVM, Logistic Regression, K-Nearest Neighbors, Naive Bayes, Decision Tree, Random Forest, and Ensemble learning. It has been run through these models before and after fine tuning. The best accuracy was found with Support Vector machines after fine tuning. Another observation is that the inclusion of feature of CDR’ i.e., Clinical Dementia Rating showed a spike in the evaluation metrics with the best accuracy being 97% achieved by Support Vector Model.

关键词： Measurement machine learning algorithms Neurons Support vector machine classification Medical services Market research Alzheimer's disease

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Hyperparameter Tuning Algorithm Comparison with machine learning algorithms

Hyperparameter Tuning Algorithm Comparison with Machine Lear...

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International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Farel Arden Cutifa Safitri Faculty of Computing President University Cikarang Indonesia

Hyperparameters are the foundation for optimizing the way machine learning algorithms supposed to learn. It is essential to have the optimal hyperparameter values for any learning algorithms. However, tuning hyperparameters can be difficult because it requires a rule of thumb and how to adjust them appropriately. This paper compares hyperparameter tuning approaches in various machine learning algorithms. Simulation results discussed six machine learning algorithms deployment, which is: Decision Tree, Gaussian Naive Bayes, Random Forest, LightGBM, Catboost, and XGBoost. For each mentioned algorithm, six hyperparameter tuning methods (random search, grid search, Bayesian Optimization, Genetic Algorithm, SHERPA, and Optuna) are embedded to evaluate their efficiency. For each test, the value range that is specified are same throughout the algorithm. The advantages of having this method are that the time taken to find the best hyperparameter value is more controlled. The objective of this research is to learn the necessity of hyperparameter tuning, and if that is the case, what hyperparameter tuning methods is the best for each algorithm. The result of this research is that Random Forest without the aid of hyperparameter tuning achieved the best performance. The Future research will be able to forgo evaluating all hyperparameter tuning methods simultaneously after identifying the most efficient way for six machine learning algorithms.

关键词： Training machine learning algorithms Simulation Clustering algorithms Classification algorithms Time complexity Information technology

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Using CatBoost and Other Supervised machine learning algorithms to Predict Alzheimer's Disease

Using CatBoost and Other Supervised Machine Learning Algorit...

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International Conference on machine learning and Applications (ICMLA)

作者： Jessica An Urbana High School Frederick USA

Alzheimer's disease is a progressive neurologic disorder that affects millions of elderly people worldwide. Most affected patients are not formally diagnosed due to the complexity of the disease and the lack of definitive diagnostic tools. machine learning algorithms are powerful in deciphering complex data patterns. This study applied and evaluated a comprehensive set of nine machine learning techniques in detecting Alzheimer's disease. The model training and testing utilized clinical and brain magnetic resonance imaging features from The Open Access Series of Imaging Studies (OASIS) of Alzheimer's disease. The input data include ordinal data such as cognitive scores and numerical data of imaging measurements. To predict Alzheimer's disease, multiple types of supervised machine learning algorithms were trained, including CatBoost, logistic regression, decision tree, random forest, Naive Bayes, SVM, gradient boosting, XGBoost, and AdaBoost. A set of model performance metrics demonstrated that most algorithms were able to perform very well with high accuracy (92-96% in a longitudinal dataset). The models using CatBoost, SVM and decision tree performed the best. The results of this study suggest that ML algorithms combining clinical cognitive assessment and brain MRI images can assist and improve Alzheimer's disease diagnosis.

关键词： Support vector machines Training machine learning algorithms Magnetic resonance imaging Brain modeling Prediction algorithms Numerical models

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Classification and Resource Generation for Bangla Emails Based on machine learning algorithms

Classification and Resource Generation for Bangla Emails Bas...

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International Conference on Computer and Information Technology (ICCIT)

作者： Md. Tariqul Islam K. M. Azharul Hasan Md. Ibrahim Hossen Institute of Information and Communication Technology Khulna University of Engineering and Technology Khulna Bangladesh Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh Department of Computer Science and Engineering University of Global Village Barishal Bangladesh

Classification of emails is an important research issue since a huge number of emails are received every day. Emails become a most objective correspondence with others. Billions of emails that are sent per day all over the world become threatening to people. A spam email can be used to pick up things from our electrical gadgets by forcing or phishing. Besides that, we receive several other less important emails. Bangla emails are very common nowadays that are facing similar problems. But due to less collection of Bangla emails important emails are not correctly classified and the receiver missed them. Considering English or other important languages, there are accessible approaches to distinguishing the emails. In this paper, we propose a classification scheme for emails written in the Bangla language. We create a Bangla email dataset and propose a multilevel classification. We found the distinguished features to classify them. Important machine learning algorithms are used to classify them.

关键词： Deep learning machine learning algorithms Unsolicited e-mail Phishing Human intelligence Text categorization Neural networks

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