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machine learning algorithms for accurate differential diagnosis of lymphocytosis based on cell population data

BRITISH JOURNAL OF HAEMATOLOGY 2019年第6期184卷 1035-1037页

作者： Bigorra, Laura Larriba, Iciar Gutierrez-Gallego, Ricardo Synlab Global Diagnost Core Haematol Barcelona Spain Pompeu Fabra Univ Dept Expt & Hlth Sci Barcelona Spain

The article presents a study which designed, developed and implemented novel machine learning algorithms (MLAs) as a support tool for lymphocyte-related diagnosis using cell population data (CPD), along with absolute lymphoid count, age and gender. Topics include stages to build the model; classification algorithms that were tested including decision trees, random forests, and k-nearest neighbour; and finding on the neural networks (NN) algorithm, based on CPD and absolute lymphoid counts.

关键词： machine learning algorithms cell population data lymphocytosis chronic lymphocytic leukaemia reactive lymphocytosis

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machine learning algorithms for air pollutants forecasting

Machine Learning algorithms for air pollutants forecasting

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International Symposium for Design and Technology of Electronics Packages (SIITME)

作者： Marius Dobrea Andreea Bădicu Marina Barbu Oana Subea Mihaela Bălănescu Geroge Suciu Andrei Bîrdici Oana Orza Ciprian Dobre BEIA Consult International Bucharest Romania University POLITEHNICA Bucharest Romania Bucharest Romania

ISBN: (数字)9781728175065

ISBN: (纸本)9781728175072

Air pollution represents an issue that raises many concerns nowadays, as it has various negative effects on the environment and the economy worldwide. Because of the rapid urbanization, cities are suffering from polluted air, so it is important to predict future air quality. For this purpose, new applications of artificial intelligence should be employed. In this paper, we will present several machine learning algorithms, the possible software that can be used for them and the applications used in the field of air quality. Based on the research in the field, we propose SVR, ARIMA and LSTM, 3 machine learning models, which can be used to predict air pollution. These algorithms have been tested using time-series for PM 10 and PM 2.5 particles. The results showed that SVR and ARIMA algorithms are the most suitable in forecasting air pollutant concentrations.

关键词： Predictive models Prediction algorithms Data models Air pollution Atmospheric modeling machine learning algorithms Forecasting

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machine learning algorithms in WSNs and its Applications

Machine Learning Algorithms in WSNs and its Applications

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Computational Intelligence and Computing Applications (ICCICA), International Conference on

作者： Archana R Raut S. P. Khandait G H Raisoni College of Engineering Nagpur India KDK College of Engineering Nagpur India

ISBN: (纸本)9781665420419

Wireless sensor network (WSN) the unique and utmost encouraging tools for monitoring the real-time applications. It has been utilized in various areas particularly for offering real-time monitoring and control applications which attempts to monitor and record the environmental parameters and takes the appropriate decisions on time in a difficult situation. In recent enlargements machine learning (ML) techniques has been used to solve different problems in WSNs to ensure that good decisions can be made in the complex situations in time. Applying ML will help in boosting the efficiency of WSNs, as well as limiting humanoid intervention or re-programming. We have studied previous work for addressing the issues in Quality of Service (QoS) provisioning in WSNs. In addition we done the survey of ML based techniques used to address the issues in WSNs in the recent era.

关键词： Knowledge engineering Wireless sensor networks machine learning algorithms Limiting Mission critical systems Humanoid robots Quality of service

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machine learning algorithms to find out the best images from the pool of blurred and unoriented images and clustering based on similarities

Machine Learning algorithms to find out the best images from...

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Emerging Innovations and Advanced Computing (INNOCOMP), International Conference on

作者： Hetan Hemant Nandre Aniket Prashant More Gaurav Narendra Patil Gaurav Rajendra Patil Pushkar Joglekar Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350376470

ISBN: (纸本)9798350376487

In the era of digital photography, the ease of capturing moments has reached unprecedented levels, rendering the creation of lasting memories a seemingly effortless task. However, not all images encapsulate the essence of cherished moments with the desired perfection. This innovative project proposes a groundbreaking solution to streamline the curation process, ensuring that only the most exceptional images are preserved in the vast sea of digital snapshots. The primary focus of this endeavor is to eliminate suboptimal captures, filtering out images plagued by blurriness or poor orientation. Furthermore, the project aims to organize these carefully curated images into coherent clusters based on their inherent similarities, creating a seamless and intuitive structure within photo collections. The central objective revolves around elevating the overall quality of photo collections by establishing a rigorous selection process. By harnessing the power of advanced techniques, such as image analysis algorithms and machine learning models, the project aims to automate the identification of optimal images. This not only enhances the efficiency of the curation process but also saves users valuable time and effort that would otherwise be spent manually sifting through a multitude of images. Through the application of cutting-edge technology, the project seeks to go beyond mere selection and extend its capabilities to categorizing the chosen images into organized clusters based on shared characteristics. This multifaceted approach ensures that users are not only presented with the best individual photos but also benefit from a well-organized and intelligently categorized photo collection. In summary, this project represents a significant leap forward in the realm of digital photography, combining innovation, efficiency, and technology to revolutionize the way we curate and preserve our most treasured memories. With a focus on automating the identification of optimal images and

关键词： Image quality Technological innovation machine learning algorithms Filtering Memory management Clustering algorithms machine learning

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machine learning algorithms Evaluation for Phishing URLs Classification

Machine Learning Algorithms Evaluation for Phishing URLs Cla...

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Habiba BOUIJIJ Amine BERQIA SSL Lab ENSIAS Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9781665437745

Phishing URL is a type of cyberattack, based on falsified URLs. The number of phishing URL attacks continues to increase despite cybersecurity efforts. According to the Anti-Phishing Working Group (APWG), the number of phishing websites observed in 2020 is 1 520 832, doubling over the course of a year. Various algorithms, techniques and methods can be used to build models for phishing URL detection and classification. From our reading, we observed that machine learning (ML) is one of the recent approaches used to detect and classify phishing URL in an efficient and proactive way. In this paper, we evaluate eleven of the most adopted ML algorithms such as Decision Tree (DT), Nearest Neighbours (KNN), Gradient Boosting (GB), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector machines (SVM), Neural Network (NN), Ex-tra_Tree (ET), Ada_Boost (AB) and Bagging (B). To do that, we compute detection accuracy metric for each algorithm and we use lexical analysis to extract the URL features.

关键词： Uniform resource locators Support vector machines Measurement Radio frequency machine learning algorithms Phishing Feature extraction

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machine learning algorithms is used for the Design and Improvement of Low-Temperature SCR Catalysts

Machine Learning Algorithms is used for the Design and Impro...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Ruijiao Tian Ping Jiang Jing Xu Wanjiang University of Technology Maanshan Anhui China

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

In order to solve the challenges of designing and improving low-temperature SCR catalysts for chemicals, in view of the shortcomings of the learning sting AHP algorithms, this study proposes an innovative design and improvement method of low-temperature SCR catalysts based on machine learning algorithms. This new scheme uses the principles of statistical information theory to accurately identify and locate key influencing factors, and accordingly performs a sensible classification of indicators to reduce possible interference. At the same time, using the unique mechanism of machine learning algorithm, the design strategy of low-temperature SCR catalyst is cleverly constructed. The empirical results show that this scheme shows significant improvement compared with the traditional AHP algorithm in terms of key performance indicators such as the accuracy of the design and improvement of the low-temperature SCR catalyst, and the processing efficiency of key factors, showing its obvious advantages. In chemical products, the design and improvement of low-temperature SCR catalysts play a crucial role, which can accurately predict and optimize the growth trend and output results of the design and improvement of low-temperature SCR catalysts for chemical products. However, when faced with complex simulation tasks, traditional AHP algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this problem, this study introduces a new idea of designing and improving the low-temperature SCR catalyst optimized by machine science learning algorithm, and accurately controls the influencing parameters through statistical information theory, and uses this as a road map for index allocation, and then uses machine learning algorithm to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly opt

关键词： machine learning algorithms Accuracy Catalysts Key performance indicator Chemical products Prediction algorithms Classification algorithms Computer security Chemicals Information theory

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machine learning algorithms to Detect Penetrations of PVDF

Machine Learning Algorithms to Detect Penetrations of PVDF

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Yayu Zhai Ping Song Xiaoxiao Chen Lab. of Biomimetic Robots and Systems Beijing Institute of Technology Beijing China

ISBN: (纸本)9781538665664;9781538665657

Polyvinylidene fluoride (PVDF) has widely used in detecting the interplanetary dust. In the case of penetration and non-penetration, the output signals of the PVDF are quite different. Detecting whether particles penetrate PVDF is a crucial issue. We create a set of experimental equipment for collecting the signals from the PVDF. The equipment consists of particle emitter, shield, conditioning circuits and data acquisition equipment. 600 experiments are conducted. Among 200 experiments, the particles penetrate PVDF. We successfully distinguish penetration of PVDF using four machine learning algorithms: Anomaly Detection (AD), Artificial Neural Network (ANN), K-Nearest-Neighbors (KNN), and Support Vector machines (SVM). We propose a unique evaluation criteria OP to evaluate the performance of four classifiers including their accuracy and computational time. The results show that ANN is the best machine learning algorithm for our problem, and AD is not suitable for our problem.

关键词： machine learning algorithms Calibration Artificial neural networks Support vector machines Classification algorithms Detectors Training

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machine learning algorithms for detecting the existence of heart disease in patients: a comparative analysis

Machine learning algorithms for detecting the existence of h...

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Sustainable Energy, Signal Processing and Cyber Security (iSSSC), IEEE International Symposium on

作者： Binita Kumari Sushreeta Tripathy Sipra Sahoo Department of Computer Application Siksha 'O' Anusandhan Deemed to be University Bhubaneswar Odisha India Department of Computer Science and Engineering Siksha 'O' Anusandhan Deemed to be University Bhubaneswar Odisha India

ISBN: (纸本)9781665490573

machine learning has grown in popularity across many industries, particularly in the health-care industry. machine learning technologies must be utilized to assist researchers and medical professionals in the early diagnosis of ailments like heart disease, which is among the world’s leading causes of death. When it comes to learning from data, machine learning algorithms shine. Since healthcare providers collect massive amounts of data on a regular basis, these algorithms have a lot of room to grow in this industry. A comparative analytical technique was used in this research study to determine which algorithm works better under the specified conditions. Using random sampling, a number of studies were conducted. The data used in this study comes from a machine learning database at the University of California, Irvine (UCI), which contains 303 cases with 14 features. Principal Component Analysis is used to scale the information gathered. Neural Network (NN), Naive Bayes, Support Vector machine (SVM), and Random forest methods are utilised to build several common models employing supervised machine learning classification techniques on scaled data. Standard performance measurements are also used for the evaluation of these methods. According to the results of the tests, Naïve Bayes outperforms SVM, random forest and NN.

关键词： Industries Heart machine learning algorithms Signal processing algorithms Support vector machine classification Artificial neural networks Signal processing

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machine learning algorithms in Recommendation System for Diagnosis of Breast Cancer According to Microwave Radiothermometry

Machine Learning Algorithms in Recommendation System for Dia...

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Alexander Losev Alexander Petrenko Institute of Mathematics and Information Technologies Volgograd State University Volgograd Russia

ISBN: (数字)9781728188409

ISBN: (纸本)9781728181134

This study is devoted to the development of a recommendation system for early diagnosis of breast cancer according to microwave radiothermometryMicrowave radiometry is a modern method of medical diagnostics, based on measuring the intrinsic electromagnetic radiation of human tissues in the infrared and microwave wavelengths. Over the past two decades, this method has proven itself in a number of branches of medicine. At the same time, a number of problems were identified. One of the most relevant of them is the development of methods and algorithms for the qualitative analysis and interpretation of thermometric data. In this paper, we propose a solution to this problem based on the creation of an appropriate recommendation system. First of all, it provides an effective solution to the classification problem, i.e. assignment of the examined patient to the corresponding diagnostic class. At the same time, the recommendation system being developed provides a qualitative interpretation of the obtained thermometric data and the justification of the proposed diagnostic solution in the user's language, i.e. diagnostician.A distinctive feature of the classifiers under development is a specific feature space built on the basis of mathematical functions simulating the nuances of the behavior of the temperature fields of patients of various diagnostic classes.

关键词： Microwave measurement machine learning algorithms Wavelength measurement Microwave theory and techniques Breast cancer Classification algorithms Microwave radiometry

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Comparison of Different machine learning algorithms for Deep Fake Detection 2

Comparison of Different Machine Learning Algorithms for Deep...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Raveena Punyani, Pooja Chhikara, Rita The NorthCap University Department of Applied Sciences Gurugram India The NorthCap University Department of Computer Science Engineering Gurugram India

ISBN: (纸本)9798350369960

Deepfake is a newly developed area of artificial intelligence technology that is widely used on social media and involves superimposing the facial features of one person over that of another. machine learning is the primary component of deepfake creation, and it has made it possible for deepfake videos and pictures to be produced much more quickly and cheaply. Although the term 'deepfakes' has a bad reputation, the technology is increasingly being used both professionally and personally. Although while it is still pretty new, recent technical developments make it harder and harder to distinguish between deep fakes and synthetic pictures. This study presents a comparative analysis of different algorithms, such as KNN, Support Vector machine, Random Forest tree, Decision or Classification tree, and Naïve Bayes algorithm. To evaluate the effectiveness of these algorithms, the dataset undergoes preprocessing before being used as input for each algorithm. Subsequently, their performance is measured by calculating and comparing metrics like F1-score, recall, precision, and accuracy. © 2023 IEEE.

关键词： classification problem Decision tree KNN machine learning algorithms Naïve Bayes Random Forest Support Vector machine

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