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检索条件"主题词=Machine Learning Algorithms"

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Application of machine learning algorithms for Prediction of Sinter machine Productivity

Application of Machine Learning Algorithms for Prediction of...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Arpit Mallick Subhra Dhara Sushant Rath Kalinga Institute of Industrial Technology Bhubaneshwar India Research and Development Centre for Iron and Steel Steel Authority of India Limited Ranchi India

Iron ore sinter machine productivity is one of the most important techno-economic parameters of an integrated steel plant. It depends upon the composition of different constituents of sinter like coke breeze, iron ore fines and flux which are agglomerated to produce sinter for blast furnaces. It is difficult to assess the interdependence of these constituents and their effect on sinter productivity from physical experimentation. In this paper, machine learning and data analytics approach is introduced to predict the sinter machine productivity. Industrial data of sinter machine productivity has been collected from an integrated steel plant. A linear regression model was first developed to predict sinter machine productivity with the composition of constituent materials of the agglomerate as model inputs. Artificial neural network (ANN) model correlating the input parameters with sinter productivity was also developed. It is found that the ANN model is agreeing well with measured sinter machine productivity accurately. Sensitivity analysis was carried out to identify major factors affecting sinter productivity.

关键词： Productivity Analytical models machine learning algorithms Sensitivity analysis Linear regression Artificial neural networks Predictive models

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Investigation Techniques for Rolling Bearing Fault Diagnosis Using machine learning algorithms

Investigation Techniques for Rolling Bearing Fault Diagnosis...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Abdelelah Almounajjed Ashwin Kumar Sahoo Mani Kant Kumar Muhannad Drak Alsebai C.V Raman Global university Bhubaneswar Odisha India Faculty of Engineering and Technology Jain University Bangalore India

ISBN: (纸本)9781665412728;9781665448345

Fault's diagnosis prior to its existence and the entire shut-down is critically important for the industry. Diagnosing the faults based on condition monitoring methods and artificial intelligence techniques is extremely potent. Detecting the bearing fault is a challenging task at the incipient level, which can lead to the malfunctioning of the machine. The usage of deep learning and machine learning algorithms produces a remarkable outcome in the domain of bearing fault diagnosis. In addition, the mentioned algorithms provide effective and reliable solutions for preventive maintenance. This paper summarizes the recently published works of artificial intelligence applications when it comes to bearing faults detection in rotary machines. Moreover, this work also presents several research gaps in this field.

关键词： Fault diagnosis Support vector machines Industries Deep learning machine learning algorithms Fault detection Rotating machines

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Comparative Study of machine learning algorithms for Portuguese Bank Data

Comparative Study of Machine Learning Algorithms for Portugu...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Arushi Gupta Anjali Raghav Smriti Srivastava Accenture Pune India MAIT New Delhi India NSUT New Delhi India

ISBN: (数字)9781728185293

ISBN: (纸本)9781728185309

Direct marketing involves telephone calls, personalized emails, and messages, newsletters, which catch the eye of the customer, and in turn, attract them towards the company. In this paper data of a Portuguese bank is considered. The bank makes a call to its potential customers regarding its term deposit schemes and is interested in customers that will invest in term deposits. With the advent of machine learning algorithms in prediction, comparative study of various algorithms such as Support Vector machine (SVM), Gaussian Naïve Bayes, Random Forest, Light Gradient Boosting (GBM), and Extreme Gradient Boosting is performed for the said data set. Light GBM gives the best results among these in very less processing time.

关键词： Support vector machines machine learning algorithms Boosting Telephone sets Prediction algorithms Intelligent systems Random forests

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Performance Metrices of Different machine learning algorithms

Performance Metrices of Different Machine Learning Algorithm...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Riya Shatakshi Gupta Vishvashdeep Vinit Kumar CSE GCET Greater Noida India

ISBN: (纸本)9781665438124

E-mail is one of the most significant components or applications of the internet. Without e-mail, the internet would be useless. E-mail is a convenient and speedy method of communication. Email is a common way for people to exchange personal and professional information. Spam emails and non-spam emails are the two forms of emails. The purpose of spam email detection is to distinguish spam from valid e-mail. Spam is typically defined as uninvited or unwelcome email messages. The majority of spam emails contain viruses or other dangerous software that can cause computer and network malfunctions, use network bandwidth and storage space, and cause email servers to slow down. Because of the increase in the abundance of information, spam has become a big issue for internet users in recent years. Email phishing is detected and prevented using text mining techniques. Phishers trick people into providing personal information by sending fake emails and visiting fraudulent websites. Because of the increase in the volume of unwanted emails, there is an urgent need for the development of more dependable and robust email systems. Every day, email users receive hundreds of spam emails with unique content from anonymous addresses generated by robot software agents. This paperwork focusses to compare the accuracy, precision, recall, and F1 of the following algorithms: KNN, SVM. Nave bias and Random forest using emails as input.

关键词： Support vector machines Text mining machine learning algorithms Unsolicited e-mail Text categorization Software algorithms Software agents

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Effectiveness of machine learning algorithms in Forecasting AQI

Effectiveness of Machine Learning Algorithms in Forecasting ...

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Technological Advancements and Innovations (ICTAI), International Conference on

作者： Sudhanshu Pal Debanshi Pramanik Ena Jain B. Tech Computer Science & Engineering DIT University Dehradun India School of Computing DIT University Dehradun India

ISBN: (纸本)9781665420884

Air pollution has become major cause of global concern in recent times. Global warming, climate changes and many major hazardous diseases are all reasons of air pollution. Rapid industrialization, urbanization, deforestation have all accelerated the air pollution level. Air is getting polluted due to release of poisonous gases by industrialization, vehicular emissions, unplanned construction activities. Everyone is at an alarming risk whether they are children or elder. Among metro cities of the world Delhi is known as one polluted metro cities in the world and we have used AQI data of Delhi city as our testbed. This work proposes a model in which machine learning algorithms are applied to measure the future AQI data from the current and past available AQI data. We have used various algorithms like Linear Regression, Lasso Regression, Random Forest and Decision Tree to predict the future AQI with past AQI and found that Decision Tree algorithm gives best prediction with limited number of data entries and Random Forest gives best result with larger dataset.

关键词： Technological innovation machine learning algorithms Urban areas Prediction algorithms Air pollution Pollution measurement Planning

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Comparison of machine learning algorithms for Patient Handling Recognition based on Body Mechanics

Comparison of Machine Learning Algorithms for Patient Handli...

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IEEE Global Conference on Life Sciences and Technologies (LifeTech)

作者： Kodai Kitagawa Takayuki Nagasaki Sota Nakano Mitsumasa Hida Shogo Okamatsu Chikamune Wada Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Kitakyushu Japan Tohoku Bunka Gakuen University Sendai Japan Kyushu University of Nursing and Social Welfare Tamana Japan Osaka Kawasaki Rehabilitation University Kaizuka Japan Kitakyushu Rehabilitation College Miyako-gun Japan

ISBN: (数字)9781665418751

ISBN: (纸本)9781665448086

Caregivers have low back pains due to patient handling. Therefore, we have been developing a monitoring system for patient handling designed to prevent low back pains. In this study, we propose a recognition method for patient handling using inertial measurement unit (IMU) and insole pressure sensor for a wearable monitoring system. Our proposed method recognizes recommended body mechanics-based patient handling by machine learning with trunk angle and ground reaction force on feet. Accuracy of the proposed method was evaluated by the experiment in a laboratory environment. Furthermore, accuracies of 7 common algorithms for machine learning are compared for consideration of the most suitable algorithm for the proposed method. Selected 7 algorithms were artificial neural network (ANN), decision tree (DT), k-nearest neighbor (KNN), logistic, random forest (RF), naive Bayes (NB), and support vector machine (SVM). The study participants were 5 young males. Each participant performed a stand assist motion with 2 conditions (natural and body mechanics) 10 trials for each condition. Accuracies were calculated by 10-fold cross validation with 100 data. The results suggested that our proposed method could recognize 2 stand assist motions with high accuracy more than 0.9 of the time in 3 machine learning algorithms. Furthermore, the best algorithm (RF) could recognize 2 stand assist motions with 0.97 accuracy. These results indicated that the proposed method could recognize patient handling motion and could be applied to a monitoring system to prevent low back pain among caregivers. In addition, this study found the best algorithm for patient handling recognition.

关键词： Support vector machines Radio frequency Pressure sensors machine learning algorithms Pain Monitoring Random forests

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Diabetes Prediction and Classification using machine learning algorithms

Diabetes Prediction and Classification using Machine Learnin...

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IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health

作者： Yogita Dubey Pushkar Wankhede Tanvi Borkar Amey Borkar Kajal Mitra Department of Electronics and Telecommunication Yeshwantrao Chavan College of Engineering Nagpur India NKP Salve Institute of Medical Sciences and Research Center and Lata Mangeshkar Hospital Nagpur India

ISBN: (数字)9781665466219

ISBN: (纸本)9781665466226

Diabetes is one of the most grievous diseases in the world which has no remedy to cure it after a particular stage. Over 422 million people in the world are diagnosed with diabetes and many others are at jeopardy. Thus, timely diagnosis and medication is required to inhibit diabetes and its associated health problems. In this paper a framework is proposed for diabetes diseases prediction and classification using machine learning (ML) algorithms. The dataset is collected from Shalinitai Meghe Hospital and Research Centre, Nagpur, NKP Salve Institute of Medical Sciences and Research Centre and Mendeley Data. Four different ML algorithms Logistic Regression, Naive Bayes, Support Vector machine and Random Forest are applied and evaluated the model with various quantitative measures. The motive of this framework is to diagnose diabetes early and to save money and time of a patient using various machine learning approaches.

关键词： Support vector machines machine learning algorithms Prediction algorithms Diabetes Classification algorithms Information technology Medical diagnostic imaging

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A Comparative Analysis of machine learning algorithms on Malicious URL Prediction

A Comparative Analysis of Machine Learning Algorithms on Mal...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： D. Vaishnavi S. Suwetha Y.Bevish Jinila R. Subhashini S.Prayla Shyry School of Computing Sathyabama Institute of Science and Technology India

Phishing is a form of fraudulent behavior where an entity or a person mimics to be a valid user. Phishing has become popular in cyber space and it is used as a tool for deceiving the users. Most of the phishing messages are difficult to interpret. In the existing literature, there are many schemes have addressed the phishing issue. Yet, there is no concrete solution to thwart such attacks. Taking this into consideration, to detect phishing threats, a machine learning-based prediction scheme is proposed in this article. From the experimental analysis, it is identified that logistic regression outperforms the other schemes in terms of accuracy and error rate. The accuracy on URL prediction with logistic regression is 97%.

关键词： Uniform resource locators Measurement machine learning algorithms Error analysis Phishing MIMICs Tools

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Predicting the Presence of Amphibians Near Road Construction Sites Using Emerging machine learning algorithms

Predicting the Presence of Amphibians Near Road Construction...

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Artificial Intelligence and machine Vision (AIMV), International Conference on

作者： Ipsita Goel Siddharth Rajesh Goradia Anil Kumar Kakelli SCOPE VIT Vellore TN India

ISBN: (纸本)9781665442121

The construction of dense road networks exerts a drastic influence on the persistence of amphibian species inhabiting the adjacent areas. Preventing any arising conflicts between nature conservation and urbanization is vital. We suggest an efficient system to predict the existence of amphibians in the vicinity while constructing roads and planned infrastructure projects. This model uses the XGBoost framework. Moreover, we implement various classification techniques such as XGBClassifier with GridSearchCV and without GridSearchCV, Naive Bayes Classifier, Decision Tree, KNN Classifier, SVM, and RidgeClassifier and compare their performances. Comparative review of these classifiers shows that XGBClassifier with GridSearchCV outperforms the other classification algorithms with high accuracy. The factors thus identified should be taken into account for sustainable urban planning.

关键词： Support vector machines machine learning algorithms Roads machine vision Urban planning Naive Bayes methods Classification algorithms

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Dynamic Data Mining for Multidimensional Data Based On machine learning algorithms

Dynamic Data Mining for Multidimensional Data Based On Machi...

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International Conference on Information Systems and Computer Networks (ISCON)

作者： Suvendu Kumar Jena Priyabrata Sahu Sasmita Mishra Indira Gandhi Institute of Technology Odisha India

ISBN: (纸本)9781665447874

While existing data mining methods and machine learning algorithms need a substantial quantity of data to train, more data must be acquired before they can be used. The intricacy of the model affects the size of the file. when analyzing data, like clinical trials, is difficult or costly, data-driven learning may be inefficient or overly basic It is important to have topic-specific subject knowledge throughout the concept-development process in biomedical research. Using current visualization techniques helps scale machine learning and visual data mining algorithms, much as it does with machine learning and visual data mining. Utilizing a new method, multidimensional data visualization is utilized to help the end consumer to better comprehend their data by incorporating machine learning and data mining. Enhancing model building efficacy via various data gathering techniques, such as variable data collection, data labeling, and data change is provided through these feedback designations. In theory, the greater array of techniques will make it possible to utilize lower sample sizes, which means it will be more relevant for larger data sets, which, if possible, may have a significant effect on certain circumstances where sample sizes are difficult to collect. The two applications involved in this experiment are both character recognition software: one to decipher characters' written content, and the other to determine the author's intended meaning (regression). In spite of this, it was shown that machine learning algorithms, with or without alternate data visualization, may provide similar results with less data.

关键词： Visualization machine learning algorithms Heuristic algorithms Data visualization machine learning Software Data models

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