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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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machine learning algorithms in Quantum Computing: A Survey

Machine Learning Algorithms in Quantum Computing: A Survey

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International Joint Conference on Neural Networks (IJCNN)

作者： Somayeh Bakhtiari Ramezani Alexander Sommers Harish Kumar Manchukonda Shahram Rahimi Amin Amirlatifi Computer Science and Engineering Dept. Mississippi State University Starkville US Dave C. Swalm School of Chemical Eng Mississippi State University Starkville US

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

machine learning (ML) aims at designing models that learn from previous experience, without being explicitly formulated. Applications of machine learning are inexhaustible, including recognizing patterns, predicting future trends and making decisions, and they are capable of handling sizable quantities of multi-dimensional data in the form of large vectors and tensors. To perform these operations on classical computers, however, requires vast time and computational resources. Unlike the classical computers that rely on computations using binary bits, Quantum Computers (QC) benefit from qubits which can hold combinations of 0 and 1 at the same time via superposition and entanglement. This makes QCs powerful at handling and post processing large tensors, making them a prime target for implementing ML algorithms. While several models used for ML on QCs are based on concepts from their classical computing counterparts, utilization of the QC's potential has made them the superior of the two. This paper presents an overview of the current state of knowledge in application of ML on QC, and evaluates the speed up, and complexity advantages of using quantum machines.

关键词： Quantum computing machine learning algorithms Quantum entanglement Complexity theory machine learning Computational modeling Parallel processing

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machine learning algorithms applied to Genetic Promoter Sequences Recognition

Machine learning Algorithms applied to Genetic Promoter Sequ...

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International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)

作者： Omar Guzmán-Ruiz Manuel Mejía-Lavalle Alicia Martínez Yasmín Hernández Tecnológico Nacional de México / CENIDET Cuernavaca Morelos MEX

A novel approach presents to recognize promoter sequences using supervised learning algorithms. For the proposed approach was possible to recognize correctly more than 95% of the tested cases, the cases were previousl... 详细信息

ISBN: (数字)9781728199047

ISBN: (纸本)9781728199054

关键词： machine learning algorithms Mechatronics Databases Supervised learning Genomics Bioinformatics Automotive engineering

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Forecasting frost risk in forest plantations by the combination of spatial data and machine learning algorithms

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AGRICULTURAL AND FOREST METEOROLOGY 2021年 306卷 108450-108450页

作者： Diniz, Ecio Souza Lorenzon, Alexandre Simoes Martins de Castro, Nero Lemos Marcatti, Gustavo Eduardo dos Santos, Osmarino Pires de Deus Junior, Jose Carlos Lopes Cavalcante, Rosane Barbosa Fernandes-Filho, Elpidio Inacio Amaral, Cibele Hummeldo Univ Fed Vicosa Dept Engn Florestal Lab Sensoriamento Remoto & Geoproc Campus UFV S-N BR-36570000 Vicosa MG Brazil Univ Fed Sao Joao Del Rei UFSJ CSL Dept Agr Sci Km 47MG424 Rd BR-35701970 Sete Lagoas MG Brazil CMPC Celulose Rio Grandense Guaiba RS Brazil ITV Inst Tecnol Vale Belem Para Brazil Univ Fed Vicosa Dept Solos Lab Geoproc LabGeo Campus UFV S-N BR-36570000 Vicosa MG Brazil

Brazil is one of the leading timber producers in the world. However, in South Brazil, frost events frequently cause damage and reduce yield in forest plantations, a situation predicted to become more common under global change scenarios. This raises the need for low cost and efficient tools, such as machine learning algorithms to improve forecasting of frost risk. This study used machine learning algorithms to create zoning classifications forecasting frost risk for forest plantations located in the south-central region of Rio Grande do Sul State, Brazil. For this, we gathered and processed data from a local geodatabase (i.e. high-spatial-resolution contour lines, hydrography, and forest stands limits) comprising 30 management units with consistent historical data of frost occurrence. Then, we generated possible local-scale predictors of frost occurrence, which included longitude, latitude, elevation, relative altitude, relief orientation, and Euclidean distance from hydrography. We carried out tests of three machine learning classifiers (Random Forest - RF;Support Vector machine-SVM and Multi-layer Perceptron-MLP) in order to determine which would most accurately predict frost occurrence. We found that RF provided the highest accuracy (> 90%), as well as the smallest percentages of class-specific errors (i.e. commission and omission errors), when compared to SVM and MLP. Latitude was the most important predictor of frost occurrence when using RF. Conversely, MLP performed worst, especially for classifying frost occurrence versus non-occurrence, and therefore had the highest percentage of class-specific errors. Our findings lead us to conclude that RF is the most proficient algorithm for forecasting frost occurrence from local-scale geomorphological data, without the need for high-cost investment in micro-meteorological sensors to monitor climate frost events linking temperature to plant damage. With increasing global climate extreme events, accurate risk zoning is

关键词： machine learning algorithms Climatic forecast Forest production Geoprocessing Frost Events

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A Comparative Study of Different machine learning algorithms on Bitcoin Value Prediction

A Comparative Study of Different Machine Learning Algorithms...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Mayukh Samaddar Rishiraj Roy Sayantani De Raja Karmakar Techno International New Town Kolkata India

ISBN: (数字)9781728157917

ISBN: (纸本)9781728157924

machine learning is growing rapidly and has made many theoretical breakthroughs which find its application in many fields. Bitcoin is a very secure, decentralized, peer to peer currency with no third-party involvement. The price prediction of Bitcoin in the following years is a difficult task. The objective is to take a dig in the prediction of the future prices, dealing with real world data. A comparative study of the results produced by different machine learning models, along with graphs for epoch vs price, error and accuracy for each model using both linear and non-linear functions is done. We are using both neural network algorithms, such as artificial neural network (ANN), recurrent neural network (RNN) and convolutional neural network (CNN), as well as some famous supervised learning algorithms such as Random Forest (RF) and k-nearest neighbors (k-NN), to form the analysis. The time price prediction graphs and the epoch loss accuracy graphs are used for the analysis of each algorithm working on the same data and produces different results. Finally, the best suited algorithm are used for the prediction of future Bitcoin price.

关键词： machine learning algorithms Recurrent neural networks Supervised learning Bitcoin Tools Prediction algorithms Task analysis

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Audio Emotion Recognition by Deep Neural Networks and machine learning algorithms

Audio Emotion Recognition by Deep Neural Networks and Machin...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： R Raja Subramanian Yalla Sireesha Yalla Satya Praveen Kumar Reddy Tavva Bindamrutha Mekala Harika R. Raja Sudharsan Kalasalingam Academy Of Research And Education Krishnankoil Tamil Nadu India. M. Kumarasamy College of Engineering Karur Tamil Nadu India.

ISBN: (纸本)9781665428309

One of the most stimulating tasks is speech emotion recognition. In our work, to make use of sound files as input for machine learning algorithm (SVM) and deep learning algorithm (CNN-1D,2D), for identification of emotions using sample audio files from the Berlin dataset (EMO-DB), we introduced an architecture that excerpts the MFCC and Mel-spectrogram from those audio files. To achieve an improvement in classification performance, we applied an incremental methodology to our initial model. Both machine learning and deep learning models can work directly with raw data. Based on our tentative results, the new development set is initiated for our existing framework of EMO-DB by the best-performing model. The proposed framework obtained 92.5% performance with 535 samples of EMO-DB in 7 classes. It is one of the desirable ways to recognize emotions. We used a neural network in deep learning for better results, which helps to extract the right feature from audio files for emotion classification.

关键词： Deep learning Support vector machines Emotion recognition machine learning algorithms Neural networks Speech recognition Feature extraction

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Smart portable electronic nose system in combination with machine learning algorithms for the intelligent discrimination of fire debris

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Chemometrics and Intelligent Laboratory Systems 2025年 264卷

作者： José Luis P. Calle Tomasz Dymerski Marta Ferreiro-González Miguel Palma Department of Analytical Chemistry Faculty of Sciences University of Cadiz Agrifood Campus of International Excellence (ceiA3) IVAGRO Puerto Real Cadiz 11510 Spain Department of Analytical Chemistry Faculty of Chemistry Gdańsk University of Technology 11/12 G Narutowicza Str. 80-233 Gdańsk Poland

The identification and discrimination of ignitable liquid residues (ILRs) in fire debris are crucial in forensic research for determining the intentionality of a fire. This study presents a new method using a portable sensor-based electronic nose (eNose) combined with machine learning (ML) algorithms for automated ILR classification. Six substrates (vinyl, nylon, linoleum, polyester, wood, and cotton) were burned with different ignitable liquids (gasoline, diesel, ethanol, and charcoal starter with kerosene), and samples were collected at intervals from 0 to 48 h after the fire had extinguished. Sensor responses from multiple sensors (SO 2 , H 2 S, CO, IRR, NO 2 , TBM, NH 3 , and ethanol) were collected over a duration of 140 s. The data were preprocessed using the first derivative and Savitsky-Golay filter, followed by low-level data fusion. A variable selection using the Boruta algorithm was applied, and both reduced and non-reduced matrices were used to train ML models. For detecting the presence of ILRs, random forest (RF) and support vector machine (SVM) models achieved 100 % accuracy. For discriminating between ILR types, the best performance was achieved by the RF model using the reduced matrix, correctly classifying 94.44 % of the samples. Only four sensors (SO 2 , H 2 S, CO, IRR) were necessary, indicating the potential for an optimized eNose design. This method offers advantages over traditional techniques, such as faster analysis, lower cost, and greater portability. Additionally, a web application was developed to allow users to automatically characterize fire debris using the algorithms.

关键词： Sensors Electronic nose machine learning algorithms Fire debris Ignitable liquids residues Petroleum-based products

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Application of Statistical machine learning algorithms for Classification of Bridge Deformation Data Sets

Application of Statistical Machine Learning Algorithms for C...

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Annual IEEE Systems Conference

作者： Juan C. Avendano Luis Daniel Otero Carlos Otero Florida Institute of Technology Melbourne FL USA

This paper presents the design and development of a structural health monitoring (SHM) system specifically tailored for transportation infrastructure components such as bridges. If focuses mainly the application of statistical machine learning (ML) algorithms to classify deformation datasets of a bridge. A model of a steel bridge was constructed and contactless sensors were placed to collect deformation data. Four loads were applied at each of the pre-defined four locations identified to represent heavy loads across the real bridge. Computer simulation in ANSYS and application of gradient boosting neural networks were performed to produce a comparative and predictive analysis of the behavior of transportation infrastructures, which can be used to understand the health of the structure and make informed decisions. Deformation levels at 100 critical locations on the bridge model were collected in each experiment by using sensors. The experiments were repeated to get average data for processing. Python programming language was used for coding and the analysis was performed in a Google Collaboratory Notebook. Development and training of the models were done using the Pycaret, which is a Python based framework that supports a variety of ML tools. Performance of each ML technique was evaluated by means of the accuracy. The final is capable of simulating multiple load conditions on structures, identifying possible failure points, and detecting and predicting failure scenarios. Both hardware and software implementations of a model of a bridge were performed as a pilot project to validate the proposed system.

关键词： Bridges Deformable models machine learning algorithms Computational modeling Loading Predictive models Data models

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Deepfake Detection Using machine learning algorithms

Deepfake Detection Using Machine Learning Algorithms

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Md. Shohel Rana Beddhu Murali Andrew H. Sung School of Computing Sciences and Computer Engineering The University of Southern Mississippi Hattiesburg MS USA

ISBN: (纸本)9781665424219

Deepfake, a new video manipulation technique, has drawn much attention recently. Among the unlawful or nefarious applications, Deepfake has been used for spreading misinformation, fomenting political discord, smearing opponents, or even blackmailing. As the technology becomes more sophisticated and the apps for creating them ever more available, detecting Deepfake has become a challenging task, and accordingly researchers have proposed various deep learning (DL) methods for detection. Though the DL-based approach can achieve good solutions, this paper presents the results of our study indicating that traditional machine learning (ML) techniques alone can obtain superior performance in detecting Deepfake. The ML-based approach is based on the standard methods of feature development and feature selection, followed by training, tuning, and testing an ML classifier. The advantage of the ML approach is that it allows better understandability and interpretability of the model with reduced computational cost. We present results on several Deepfake datasets that are obtained relatively fast with comparable or superior performance to the state-of-the-art DL-based methods: 99.84% accuracy on FaceForecics++, 99.38% accuracy on DFDC, 99.66% accuracy on VDFD, and 99.43% on Celeb-DF datasets. Our results suggest that an effective system for detecting Deepfakes can be built using traditional ML methods.

关键词： Training Deep learning machine learning algorithms Computational modeling Feature extraction Task analysis Informatics

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Prediction of residual stress in electron beam welding of stainless steel from process parameters and natural frequency of vibrations using machine-learning algorithms

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第11期235卷 2008-2021页

作者： Das, Debasish Das, Amit Kr Pratihar, D. K. Roy, G. G. Indian Inst Technol Kharagpur Kharagpur 721302 W Bengal India

In the present study, machine learning algorithms have been used to predict residual stress during electron beam welding of stainless steel using the information of input process parameters and natural frequency of vibrations. Accelerating voltage, beam current and welding speed have been considered as input process parameters. Both residual stress and natural frequencies of vibration of the weld obtained using each set of the input parameters are measured experimentally. A number of machine learning algorithms, namely M5 algorithm-based Model Trees Regression, Random forest, Support Vector Regression, Reduced Error Pruning Tree, Multi-layer perceptron, Instance-based k-nearest neighbor algorithm, and Locally weighted learning have been used for the said purpose. Support vector regression and Locally weighted learning are found to perform consistently good and bad, respectively. The predicted welding residual stresses have been validated experimentally through X-ray diffraction (XRD) and good agreements are obtained. In addition, statistical tests are conducted, and the estimated reliability values of the employed models are analyzed through Monte-Carlo simulations.

关键词： Electron beam welding welding residual stresses natural frequencies machine learning algorithms Monte-Carlo simulations

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