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International Conference on Microelectronics, ICM

作者： Rumana Islam Esam Abdel-Raheem Mohammed Tarique University of Windsor Windsor Canada Univ. of Sci. and Tech. of Fujairah Fujairah United Arab Emirates

ISBN: (纸本)9781665408400

This paper presents a cough sound-based fast, automated, and noninvasive COVID-19 detection system to discriminate the cough sounds of the COVID-19 patients from the healthy individuals. The proposed system extracts an acoustic feature called chromagram from the cough sound samples and applies it to the input of a classifier algorithm. Two artificial neural network (ANN) based classifiers namely convolutional neural network (CNN) and deep neural network (DNN) are modeled for this purpose. The simulation results show that the proposed system achieves an accuracy of 92.9% and 91.7% with CNN and DNN respectively. The performance comparison of the proposed system with two popular machine learning algorithms namely support vector machine (SVM) and k-nearest neighbor (kNN) are also presented in this work.

关键词： COVID-19 Support vector machines Deep learning machine learning algorithms Simulation Feature extraction Acoustics

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Design of face detection and recognition system to monitor students during online examinations using machine learning algorithms

Design of face detection and recognition system to monitor s...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： M. Geetha R.S. Latha S.K. Nivetha S. Hariprasath S. Gowtham C.S. Deepak Kongu Engineering College Erode Tamilnadu India

Today's pandemic situation has transformed the way of educating a student. Education is undertaken remotely through online platforms. In addition to the way the online course contents and online teaching, it has also changed the way of assessments. In online education, monitoring the attendance of the students is very important as the presence of students is part of a good assessment for teaching and learning. Educational institutions have adopting online examination portals for the assessments of the students. These portals make use of face recognition techniques to monitor the activities of the students and identify the malpractice done by them. This is done by capturing the students' activities through a web camera and analyzing their gestures and postures. Image processing algorithms are widely used in the literature to perform face recognition. Despite the progress made to improve the performance of face detection systems, there are issues such as variations in human facial appearance like varying lighting condition, noise in face images, scale, pose etc., that blocks the progress to reach human level accuracy. The aim of this study is to increase the accuracy of the existing face recognition systems by making use of SVM and Eigenface algorithms. In this project, an approach similar to Eigenface is used for extracting facial features through facial vectors and the datasets are trained using Support Vector machine (SVM) algorithm to perform face classification and detection. This ensures that the face recognition can be faster and be used for online exam monitoring.

关键词： Support vector machines machine learning algorithms Face recognition Education Classification algorithms Monitoring Portals

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Detection of Dangerous Web Pages Based on the Analysis of Suicidal Content Using machine learning algorithms

Detection of Dangerous Web Pages Based on the Analysis of Su...

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IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference (EIConRusNW)

作者： Maxim Lyovkin Aleksey A. Frolov Egor Perminov Moscow Engineering Physics Institute National Research Nuclear University MEPhI Moscow Russian Federation

ISBN: (数字)9781665404761

ISBN: (纸本)9781665446426

Nowadays, the task of preventing suicide is one of the priorities in the health sector. Therefore, it is important to identify people prone to suicide at an early stage. This article discusses the possibility of real-time detection of visited websites containing suicidal statements. The classification of web pages is based on the analysis of the text contained on it. This work can be divided into two parts: creating a browser extension and the server. The extension collects information about the content of the web pages visited by the user and transmits it to the server. The page classification process takes place on the server. In the final part of this work, a comparison of the effectiveness of detecting suicidal websites using various machine learning algorithms is presented.

关键词： Schedules machine learning algorithms Web pages Real-time systems Servers Task analysis Text processing

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Laser induced-breakdown spectroscopy (LIBS) combined with machine learning algorithms: An accurate tool to identify primed seeds

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Microchemical Journal 2025年 214卷

作者： Córdoba Figueroa, Mónica Eliana Ferreira, Dennis Silva Rezende, Brunna Rithielly Batista, Thiago Barbosa Pereira-Filho, Edenir Rodrigues Pereira Sartori, Maria Márcia Amaral da Silva, Edvaldo Aparecido Department of Plant Production São Paulo State University (UNESP) São Paulo Botucatu Brazil Group of Applied Instrumental Analysis (GAIA) Department of Chemistry Federal University of São Carlos (UFSCar) P.O. Box 676 São Paulo State São Carlos 13565-905 Brazil

The priming technique is commonly used to enhance and standardize seed germination. Its effectiveness is assessed through germination uniformity and vigor tests. These tests require trained analysts and are time-consuming, highlighting the need for faster, more accurate methods. This study analyzed an innovative technique capable of distinguishing between primed and unprimed tomato seeds based on their elemental composition using Laser-Induced Breakdown Spectroscopy (LIBS), principal component analysis (PCA) and machine learning algorithms: linear, quadratic and mixture discriminant analyzes (LDA, QDA, MDA), and nearest neighbor methods (KNN). Germination and vigor tested were to compare the effects of priming. The seed analyzed using LIBS predominantly displayed chemical emission lines corresponding to potassium, calcium and magnesium. PCA reduced the variables to two components, revealing two distinct groups and explaining 94.4 % of the variability. The LDA model, with a sensitivity (92.98 %), specificity (93.75 %), and error rate (0.1554), demonstrated superior performance in classifying primed and unprimed seeds using the LIBS technique. The biplot revealed that the seeds subjected to priming exhibited the intended effects. Consequently, the LIBS technique, combined with PCA and machine learning algorithms, demonstrated significant potential for distinguishing and selecting primed seeds. © 2025 Elsevier B.V.

关键词： Elemental composition machine learning algorithms PCA Solanum lycopersicum L. Vigor

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Preliminary Diagnosis of COVID-19 Based on Cough Sounds Using machine learning algorithms

Preliminary Diagnosis of COVID-19 Based on Cough Sounds Usin...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Arup Anupam N Jagan Mohan Sudarsan Sahoo Sudipta Chakraborty NIT Silchar

The proposed work is focused on COVID-19 classification of cough sounds based on machine learning which is used to differentiate COVID-19 coughs from non COVID-19 and healthy coughs. It follows a non-contact based screening test which is very easy to apply being non-invasive and simply carried out within the boundaries of home so that the medical testing centers are not over flooded with patients and there is an overwhelming pressure because of maintenance of those patients with shortage of adequate infrastructure facilities. The dataset used in this study has been derived from the Coswara database which comprises of around 160 infected and 480 healthy individuals. Therefore, Artificial Intelligence based machine learning classifiers were used as an alternative means of diagnosis. Logistic regression (LR), K- Nearest neighbor (KNN), support vector machines (SVM), decision tree algorithms were used as classifiers in the proposed work. The results of this study show that the SVM classifier turned out to be the best in comparing among the COVID-19 and non COVID- 19 coughs with area under receiver operating characteristic curve (ROC) of 0.98. The novelty in the proposed work includes the collection of dry cough samples which would aid in preliminary diagnosis of the infection. This form of classification can also be implemented in a smart phone after performance evaluation from medical authorities.

关键词： COVID-19 Support vector machines Performance evaluation machine learning algorithms machine learning Receivers Medical diagnostic imaging

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A Review on Predicting Autism Spectrum Disorder(ASD) meltdown using machine learning algorithms

A Review on Predicting Autism Spectrum Disorder(ASD) meltdow...

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International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT)

作者： Sara Karim Nazina Akter Muhammed J. A. Patwary Md. Rashedul Islam International Islamic University Chittagong

ISBN: (纸本)9781665495233

Autism Spectrum Disorder (ASD) is a well-known mental disorders that prevails in the ability of a person’s social communication. The significance of early diagnosing drew the attention of researchers to use different machine learning-based procedures. Many analyses are done with the help of machine learning (ML) techniques to foresee meltdowns of autism together with Support Vector machines, Random Forests, Naive Bayes, K-nearest Neighbors and many more. This paper gives a wide-spread review of papers applying machine learning in predicting ASD, along with algorithms for data analysis and classification. More than 80 research papers are considered, and the articles are assembled from the internet. Finally 48 research articles are coped up with the prerequisites in this study. The main goal of this review is to distinguish and mark out the machine learning trends in ASD literature and show the way to researchers curious in expanding the core of predicting ASD data and observe momentous research patterns in the field of ML. This paper will be a guideline to future researchers who are willing to work in the field of predicting ASD meltdown.

关键词： Support vector machines Autism Silver machine learning algorithms Mental disorders machine learning Predictive models

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Improved machine learning algorithms for Optimizing Coherent Pulse Stacking Amplification

Improved Machine Learning Algorithms for Optimizing Coherent...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Weizhi Du Eunjeong Hyeon Hanzhang Pei Zhengyu Huang YeonJoon Cheong Siyuan Zheng Almantas Galvanauskas Center for Ultrafast Optical Science University of Michigan Ann Arbor Michigan USA University of Michigan Ann Arbor Michigan USA University of Michigan Ann ArborMichiganUSA48109

We apply momentum stochastic parallel gradient descent (MSPGD) and policy gradient algorithms to optimize coherent pulse stacking (CPS), and demonstrate their increased effectiveness compared to traditionally used sto... 详细信息

ISBN: (纸本)9781943580910

关键词： Gradient methods Electric potential Analytical models machine learning algorithms Laser noise Stacking Lasers and electrooptics

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Comparative Evaluation of machine learning algorithms with Parameter Optimization and Feature Elimination for Fraud Detection

Comparative Evaluation of Machine Learning Algorithms with P...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Yunus Koç Mustafa Çetın Information Institude Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9781665442329

machine learning (ML) algorithms could be performed efficiently for lots of areas range from education, medicine, defense industry to consumer applications and finance. Especially data classification in finance area has become a striking part of literature recently. Especially, data classification in finance area has become a striking part of literature recently. On this issue,5 well known ML algorithms which are Logistic Regression Classifier, Decision Tree Classifier, AdaBoost Classifier, K Nearest Neighbor (KNN) Classifier and Support Vector Classifier (SVC) are evaluated by using “Credit Card Fraud Detection” dataset to deal with Fraud and Non-Fraud classification. After all algorithms are performed, it is seen that SVC has the best f1 and precision-recall scores. Moreover, ANOVA is more useful strategy to eliminate irrelevant features compared to Mutual Information method for the dataset. Optimization of model parameters are also critical factor improving classification performance.

关键词： machine learning algorithms Static VAr compensators Finance Support vector machine classification Feature extraction Classification algorithms Optimization

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Design of Short-Term Wind Production Forecasting Model using machine learning algorithms

Design of Short-Term Wind Production Forecasting Model using...

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International Symposium on Advanced Topics in Electrical Engineering (ATEE)

作者： Marius Eugen ȚIBOACĂ Sorina COSTINAȘ Petrică RĂDAN Faculty of Power Engineering University Politehnica of Bucharest S.C. Tractebel Engineering S.A

Short-term (24h) wind production forecast is mainly used in energy trading in the day-ahead market (DAM) and intraday market (IDM). The bid strategy of the market participants (wind producers) is based on wind production forecast which is one of the most important factors from an economic point of view. This paper presents the results of the proposed wind forecast model based on wind production data, weather data and supervised machine learning algorithms. The forecast model is built from scratch in Jupyter Lab with Python and Scikit-learn. In the development process of the forecast model, the most important algorithms (regression techniques), such as Linear regression, Ridge regression, Polynomial Ridge regression (order 4), Multilayer Perceptron regression, Decision Tree regression and Gradient Boosting regression are used. The data was collected, pre-processed, and used for the machine learning (ML) algorithms to prove the feasibility of the artificial intelligence applied to this field of work, with the final goal of improving the offers of the wind producers on the available energy markets. From the analysis of the wind production forecast results, it concluded that the most accurate model is the Polynomial Ridge Regression algorithm (order 4) with an error of 16.41%, measured with normalized mean absolute error (NMAE). The model accuracy could be improved by using deep learning algorithms such as Long-Short Term Memory (LSTM) or/add more features to the forecast model (forecast of the real wind speed).

关键词： Wind machine learning algorithms Production Predictive models Wind power generation Multilayer perceptrons Data models

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A Performance Evaluation of machine learning algorithms for Emotion Recognition through Speech

A Performance Evaluation of Machine Learning Algorithms for ...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Biswajeet Sahu Hemanta Kumar Palo Sachi Nandan Mohanty YBN University Jharkhand India ITER Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha College of Engineering Pune Pune India

This paper aims to recognize the human expressive states from their voice samples. It intends to extract a few reliable features and combine them intelligently for the said task for effective recognition. Initially, it extracts a few sub-band spectral properties from voice samples containing emotional information. Further, the pitch and its standard deviation along with the log-energy features have been extracted to develop an efficient combinational model. The chosen features are complementary, hence expected to increase the available emotional information. To validate the combinational framework, several machine learning algorithms (MLAs) have been simulated and compared. Among the classifiers, the Random Forest (RF) has outperformed all others in terms of classification accuracy whereas the Decision Tree remains computationally least expensive.

关键词： Radio frequency Support vector machines machine learning algorithms Speech recognition Vegetation Feature extraction Reliability

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