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

作者： Ruju Shah Vrunda Shah Anuja R. Nair Tarjni Vyas Shivani Desai Sheshang Degadwala Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad Gujarat India Department of Computer Engineering Sigma Institute of Engineering Vadodara Gujarat India

ISBN: (纸本)9781665482721

Accurate software work estimates is essential to the planning, management, and execution of a successful project on schedule and within budget. The necessity for accurate software work estimates is something that will never go away since both overestimation and underestimate provide substantial barriers to the development of additional software (SEE). Research and practise are aimed at finding the machine learning estimating technique that is most successful for a given set of criteria and data. This is the goal of the research and practise. Most academics working in a particular subject are not aware of the findings of previous studies that investigated different approaches to effort estimate in machine learning. The primary purpose of this investigation is to aid researchers working in the field of software development by assisting them in determining which method of machine learning produces the most promising effort estimate accuracy prediction.

关键词： Measurement Schedules Maximum likelihood estimation machine learning algorithms machine learning Predictive models Aerospace electronics

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Predictive analysis on severity of Non-Alcoholic Fatty Liver Disease (NAFLD) using machine learning algorithms

Predictive analysis on severity of Non-Alcoholic Fatty Liver...

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International Conference on Emerging Technologies, ICET

作者： Muhamamd Haseeb Aslam Syed Fawad Hussain Raja Hashim Ali MDS Lab Faculty of Computer Science & Engg. GIK Institute of Engg. Sciences & Tech. Khyber Pakhtunkhwa Pakistan AI Research Lab Faculty of Computer Science & Engg. GIK Institute of Engg. Sciences & Tech. Khyber Pakhtunkhwa Pakistan

ISBN: (纸本)9781665459938

Fatty Liver Disease (FLD) is a frequent clinical impediment that is linked with high weariness and mortality. Despite that, an early prediction and diagnosis provide the patient with suitable treatment. For this, we aim to develop efficient machine learning (ML) models for the timely prognosis of FLD. We propose the use of Support Vector machine (SVM), Logistic Regression, Random Forest (RF), Naive Bayes, and Multi-Layer Perceptron (MLP) on the dataset whose features are chosen by using the Mutual Information (MI) technique. This study uses the publically available dataset regarding FLD. This dataset is highly imbalanced, and to grapple with this, Synthetic Minority Oversampling Technique (SMOTE) was used. Results show that SVM performs well in comparison with the other state-of-the-art ML classifiers. In this study, we developed five models and compare the results with each other. Overall 99% accuracy is achieved by SVM and RF classification model.

关键词： Support vector machines Radio frequency machine learning algorithms Liver diseases Predictive models Feature extraction Prognostics and health management

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Performance Analysis of Image Data Analysis Using machine learning algorithms

Performance Analysis of Image Data Analysis Using Machine Le...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： T Jayapratha P Jaisurya A Jeyasri K Ramya A Vasanthakumar Department of Information Technology Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9781665491129

Across the globe, almost 2.5 quintillion bytes of data is produced every day. There is an exponential increase in the amount of data that is created. Out of this 2.5 quintillion, almost 50% constitutes image data. This image data can be exploited in order to get more precise and fruitful information, which will be employed in many fields. To analyze big data, modern technologies can be used, i.e., machine learning, deep learning, artificial intelligence, etc., this survey provides the analysis of the survey taken on image analysis using machine learning. It also discusses the achievements of the research work done in this area. Finally, we tried to analyze the accuracy achieved and the future work that can be done in this field.

关键词： Wireless communication Deep learning machine learning algorithms Image analysis Data analysis Big Data Performance analysis

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Objects Classification based on UWB Scattered Field and SEM Data using machine learning algorithms

Objects Classification based on UWB Scattered Field and SEM ...

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European Radar Conference (EURAD)

作者： Yasmina Zaky Nicolas Fortino Jean-Yves Dauvignac Benoit Miramond CNRS LEAT Universit&#x00E9 C&#x00F4 te d&#x0027 Azur France

ISBN: (纸本)9781665447232

This paper addresses the target classification problem using supervised learning techniques to discriminate spherical objects from their scattered field. The main goal is to demonstrate that pre-processed data provide a higher accuracy for classification purposes in comparison with raw data. To this end, we compare the classification performances when using raw data, in time and frequency domains, and pre-processed data by the Singularity Expansion Method (SEM). The first step is to build 3 datasets from mono- and bi-static Ultra Wide Band scattered fields; each containing 5 sphere classes with different materials. Then, we evaluate the performances of several classifiers based on machine learning algorithms trained using those constructed datasets. Applying these algorithms on the data resulting from pre-processing the scattered field with SEM proved to be more successful and allows the use of simpler classifiers.

关键词： Training Support vector machines machine learning algorithms Runtime Frequency-domain analysis Supervised learning Europe

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Faulty Class Diagnosis of Three Phase Induction Motor Bearing Using Stator Current Spectral Features and machine learning algorithms

Faulty Class Diagnosis of Three Phase Induction Motor Bearin...

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2022 9th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Kenichi Yatsugi Shrinathan Esaki Muthu Pandara Kone Yukio Mizuno Nagoya Institute of Technology Nagoya Aichi Japan

ISBN: (数字)9784886864314

ISBN: (纸本)9781665470155

Induction motors are widely used in various industries because of their robustness, which makes them attractive for applications in harsh environments. Fault detection is a topic of increasing interest, particularly for bearing faults. Various methods of bearing fault diagnosis have been proposed, including vibration, acoustic, and current signature analysis. To predict the repercussions of bearing faults, the detection of the fault class and number of faults is of particular interest. However, the above diagnostic methods only consider a single bearing fault. In this study, inclusive diagnoses were performed for detecting the class (i.e., holes and scratches) and number of faults by using the frequency-domain features of the load current. In experiments, faults of different classes and numbers were introduced to the outer raceway of bearings and tested at various load levels. The sideband frequency components of the load current were affected by the fault class and number. A support vector machine was applied to fault diagnosis using the sideband frequency components as features. Electromagnetic simulations suggested that the dependence of the feature distribution on the fault class and number could be attributed to the effect of the eddy current on the outer raceway of the bearing. The results demonstrated the robustness of the proposed diagnostic method against the class and number of bearing faults.

关键词： Support vector machines Fault diagnosis Vibrations Induction motors machine learning algorithms Velocity control Stators

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Algorithm Auditing: Managing the Legal, Ethical, and Technological Risks of Artificial Intelligence, machine learning, and Associated algorithms

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COMPUTER 2022年第4期55卷 40-50页

作者： Koshiyama, Adriano Kazim, Emre Treleaven, Philip UCL Comp Sci London WC1E 6BT England Holist Al Inc London England UCL Comp Sci Dept London WC1E 6BT England UCL Comp London WC1E 6BT England

algorithms are becoming ubiquitous. However, companies are increasingly alarmed about their algorithms causing major financial or reputational damage. A new industry is envisaged: auditing and assurance of algorithms ... 详细信息

关键词： Industries Ethics machine learning algorithms Law machine learning Companies

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RBTM: Prediction of COVID-19 from Random Blood Test with machine learning algorithms

RBTM: Prediction of COVID-19 from Random Blood Test with Mac...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： L.William Mary S. Albert Antony Raj SRM Institute of Science and Technology Kattankulathur Chennai

ISBN: (纸本)9781665487351

Recently, the ongoing global pandemic of novel coronavirus infection had a devastating impact worldwide. We develop an efficient classification model that effectively produces the predictive values of infected patients with suspicious symptoms and epidemiological history to defeat this. The research aims to use the Traditional technique to compare clinical blood tests of positive and negative cases. The diagnostic machine learning model incorporates 551random blood samples with the following parameters of the patient's demographic features, Platelet, Hemoglobin, Lymphocyte, Neutrophil, Leukocyte (WBC), Turbidimetric, Troponin-I of COVID positive and negative cases. The prediction model can achieve the classification report of Accuracy, Precision, Recall, and F1 score values. In this analysis, considering seven different algorithms for the prediction and the observation's estimation, the data is 5-fold cross-validated. Finally, investigational outcomes attain accurate predictions. Logistic Regression predicted 0.83% of accuracy. The Receiver Operator Characteristic (ROC) metrics for Logistic Regression, the Precision was 0.78 % , Recall was 0.85%, and F1-score was 0.82%, Specificity was 0.58%, and Sensitivity was 0.41%.

关键词： COVID-19 Sensitivity machine learning algorithms Pandemics Receivers machine learning Predictive models

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Water Quality Classification Using machine learning algorithms

Water Quality Classification Using Machine Learning Algorith...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Reem Alnaqeb Khuloud Alketbi Fatema Alrashdi Heba Ismail Department of Computer Science and Information Technology Abu Dhabi University Abu Dhabi United Arab Emirates

Protecting and caring for water is one of the most critical environmental problems today. This research aims to design an intelligent system using machine learning models to improve water quality and predict whether it is safe to be used as drinking water. Several models of machine learning algorithms are compared to find the best model to be used for the accuracy of prediction of water quality. In this research, we compare Decision Tree, K-Nearest Neighbor, Support Vector machine, Ransom Forest, and LightGBM models to get the best model for water potability prediction. Experimental results show that LightGBM model produced the best prediction accuracy of 99.74% on the experimental data.

关键词： Support vector machines machine learning algorithms Computational modeling Water quality Forestry machine learning Predictive models

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Detection of Brain Tumor in Medical Images Based on Feature Extraction by HOG and machine learning algorithms

Detection of Brain Tumor in Medical Images Based on Feature ...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Jayaraj Ramasamy Ruchi Doshi Kamal Kant Hiran Department of CSE Azteca University Mexico Department of CSE Sir Padampat SinghaniaUniversity Udaipur India

ISBN: (纸本)9781665453622

Computer-aided diagnostics play a big part in figuring out what’s wrong with MRI images, which helps the radiologist. As among the most prevalent and incurable cancers, brain tumors can lead to a short average lifespan in their most severe form. If the brain tumor is found and detected early, the patient may have a better chance of survival. When using medical imaging, detecting a brain tumor is notoriously challenging. Factors such as the tumor’s size, shape, and location might vary from patient to patient. A tumor’s location in the brain makes it a difficult task to identify, therefore knowing these details are critical. It’s possible that some people have tumors of high glioma type, while others have tumors of low glioma type. As a result, it is vitally important to learn how to interpret medical imaging to identify a malignant tumor. Tumor and non-tumor images in medical imaging can be differentiated using a variety of machine learning methods. In this work we have used HOG to extract features from the medical data of patients with cancer tumors and then used them to classify using machine learning algorithms.

关键词： machine learning algorithms Quantum computing Shape Malignant tumors Feature extraction Market research Task analysis

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Automotive Performance Tests Based on machine learning algorithms

Automotive Performance Tests Based on Machine Learning Algor...

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European Conference on Antennas and Propagation, EuCAP

作者： M. Geissler J. Kunisch C. Oikonomopoulos-Zachos A. Friedrich IMST GmbH Kamp-Lintfort Germany

This paper suggests an innovative approach to define and perform tests of communication systems in cars. The test concept requires the placement of the vehicle under test on a planar turntable in an anechoic chamber. Software-defined multimode transceiver modules, referred to as radio heads, are placed in a quarter circle or half circle around the car at an adequate distance. This setup allows flexible, realistic, reproducible and dynamic over-the-air testing of the cars communication systems in the sense of a virtual drive test. One key topic - which is still open - is the definition of sufficiently realistic test scenarios related to real outdoor scenarios. The full description of those scenarios would require a prohibitively large number of parameters from the network and the channel to be considered, making it impractical to perform this derivation following a classical straight-forward approach. Therefore, this paper suggests the derivation of realistic test cases via a machine learning (ML) approach: instead of attempting to create a 1:1 mapping of real scenarios into the test chamber, we propose to use ML to identify and classify critical test cases via analysis of key performance indicators (KPI) of test data and from this to create representative synthetic test cases. This approach is currently under development and open for discussion here.

关键词： machine learning algorithms Communication systems Key performance indicator Europe machine learning Transceivers Magnetic heads

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