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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, CONF-SPML 2023"

共 147 条记录，以下是51-60 订阅

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An Integrated Approach of MCDM Methods and machine learning Algorithms for Employees' Churn Prediction 3

An Integrated Approach of MCDM Methods and Machine Learning ...

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3rd international conference on Robotics, Electrical and signal processing Techniques, ICREST 2023

作者： Chowdhury, Shefayatuj Johara Mahi, Mainul Islam Saimon, Sadiqul Alam Urme, Aynur Nahar Nabil, Rashidul Hasan International Islamic University Chittagong Department of Cse Chittagong Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh International Islamic University Chittagong Department of Eee Chittagong Bangladesh

ISBN: (纸本)9798350346435

Employee churn is a notable nuisance for organizations to maintain a cost-effective position and brand strategy. This research has proposed an integrated approach to overcome this issue systematically. All employees in this study were divided into three categories using a combination of machine learning algorithms and Multi-Criteria Decision Making (MCDM) techniques. The techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Analytical Hierarchy Process (AHP) have been consolidated. The dataset's feature importance was retrieved, and AHP was used to determine the criteria that caused the most turnover. TOPSIS was given the derived criterion weights from AHP to rank every personnel according to their propensity to depart the organization. To estimate staff turnover, seven machine learning algorithms were applied. After comparing the results, the Random Forest algorithm produces the best accuracy for assessing employee churn. © 2023 IEEE.

关键词： Decision making

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signal processing Grand Challenge 2023 - E-Prevention: Sleep Behavior as an Indicator of Relapses in Psychotic Patients 48

Signal Processing Grand Challenge 2023 - E-Prevention: Sleep...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Avramidis, Kleanthis Adsul, Kranti Bose, Digbalay Narayanan, Shrikanth University of Southern California Signal Analysis and Interpretation Lab Los AngelesCA90089 United States

ISBN: (纸本)9781728163277

This paper presents the approach and results of USC SAIL's submission to the signal processing Grand Challenge 2023 - e-Prevention (Task 2), on detecting relapses in psychotic patients. Relapse prediction has proven to be challenging, primarily due to the heterogeneity of symptoms and responses to treatment between individuals. We address these challenges by investigating the use of sleep behavior features to estimate relapse days as outliers in an unsupervised machine learning setting. We extract informative features from human activity and heart rate data collected in the wild, and evaluate various combinations of feature types and time resolutions. We found that short-time sleep behavior features outperformed their awake counterparts and larger time intervals. Our submission was ranked 3rd in the Task's official leaderboard, demonstrating the potential of such features as an objective and non-invasive predictor of psychotic relapses. © 2023 IEEE.

关键词： Anomaly Detection Biosignals Psychotic Relapses Sleep Behavior Unsupervised learning

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Enhanced Pneumonia Detection Through Cough Analysis Techniques 3

Enhanced Pneumonia Detection Through Cough Analysis Techniqu...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Ramesh Babu, A. Sathvika, V. Junaid, Abdul Mannan Abhi Ram, S. Reddy, V.Akhila Shiva Shankar Reddy, K. SR University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350343632

Millions of individuals worldwide are afflicted with pneumonia, a potentially fatal respiratory infection that is an inflammatory disease that affects the air sacs in the lungs. For prompt intervention and successful treatment, pneumonia must be identified early. The proposed approach analyzes cough sounds and recognizes patterns linked to pneumonia by using 1D-CNN and sophisticated signal processing techniques. One promising non-invasive, low-cost method for detecting pneumonia may be cough sound analysis. A plethora of information about the respiratory system can be found in cough sounds, and recent advances in machine learning have made it possible to create accurate and trustworthy frameworks for the detection of pneumonia from cough sounds. Our results propose that cough sound analysis has the potential to be a valuable apparatus for pneumonia detection in resource-limited settings. Our framework is simple to utilize and does not require any specialized equipment, making it perfect for utilize in community health clinics and other inaccessible areas. The algorithm showed 91.35% test accuracy for the 82 cases of cough sound in pneumonia group and 91 cases in non-pneumonia group. © 2023 IEEE.

关键词： Audio processing Cough Analysis Deep learning Feature Extraction machine learning Pneumonia Detection

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Screening of Citrus Diseases Using Deep learning Embedders and machine learning Techniques 3

Screening of Citrus Diseases Using Deep Learning Embedders a...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Challagundla, Yagnesh Chintalapati, Lohitha Rani Tunuguntla, Trilok Sai Charan Sur, Amit Roy, Bappadittya Zhuo, Eugenia R. VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati India HIT Department of IT West Bengal India VIT-AP University School of Electronics Andhra Pradesh Amaravati India University of Santo Tomas Manila College of Information and Computing Sciences Philippines

ISBN: (纸本)9798350320749

A tropical fruit with great economic significance, citrus is cultivated and farmed in almost 150 different nations. A total of about 157.98 million tons of citrus fruit are produced worldwide. Citrus plant diseases have a major negative impact on the citrus fruit market as well as production in terms of quantity as well as quality. Attention must be paid to citrus fruits and leaves in order to promptly diagnose and treat problems;failing to do so results in severe financial loss. Citrus plant diseases cause a yearly loss of about 50% of citrus fruits at citrus farming operations. In this study, to produce the vector representation of the images, we used three deep learning image embedders namely Inception V3, VGG16, and Squeeze net. We used five machine learning models, including Random Forest, KNN, Gradient Boosting, SGD, and Neural Network with 5-fold Cross Validation, to predict citrus diseases. Using the considered dataset, the classification performance of each independently tuned ML model across all of its ensembles has been obtained. The neural network with Inception V3 as image embedder has obtained maximum average accuracy of 96.6%, 96.6% recall, 96.5% precision, and 96.5% F1 Score. Finally, our prediction model offers a reliable technique for accurate detection of citrus diseases to assist farmers. © 2023 IEEE.

关键词： Citrus fruits

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Pre Cardiac Failure Detection using different supervised machine learning Methods with CNN 3

Pre Cardiac Failure Detection using different supervised Mac...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Gupta, Amit Shaikh, Meeravali Reddy, G. Jithender Chouhan, Arun Singh Malla Reddy University School of Engineering Department of Cyber Security Telangana India Malla Reddy University School of Engineering Department of CSE Telangana India GITAM University Visakhapatnam India

ISBN: (纸本)9798350320749

For aspect, a convolution Neural Network (CNN) for cardiac arrest would help establish a better output. Whilst a cardiac arrest occurs, the limited interventions available to shop-affected person's lives are related to badly affected outcomes. Therefore, the best way to enhance affected person results and lower related healthcare prices might be to prevent cardiac arrest. This remark highlights the significance of prediction models that continuously perceive excessive-risk individuals and assist healthcare providers in providing centered care to the appropriate patient at the appropriate time for the early identification of cardiac arrest patients who may need immediate medical attention. Moreover, the significant aspect lies in the proper symmetry formation or can, say, the better scenario to compute or make a clear recognition of the image having a clear resolution of the image. Starting with a small model to determine things as simple as CNN (Convolution Neural Network). In this proposed paper, we are used in seven different supervised machine learning method for simulation of ECG data set results in different time interval with different duration and amplitude. In the CNN with a partially connected convolution layer, the image has a single neuron connected to a small layer region. The CNN (convolution Neural Network) image classification takes the input, processes it and classification is done on the dimensions organized in three dimensions width, height, and depth. © 2023 IEEE.

关键词： Convolution

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Diabetes Prediction Using Nature-Inspired Optimization Algorithms 3

Diabetes Prediction Using Nature-Inspired Optimization Algor...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Vijaya, J. Tirkey, Divyanshu Singh, Priyank Goel, Aditya International Institute of Information Technology Department of DSAI Chhattisgarh Naya Raipur India

ISBN: (纸本)9798350320749

Diabetes is a common genetic disease across the world. It is a chronic disease spurred by either production of insufficient production of insulin by the pancreas or less insulin use by the body. Insulin maintains the blood sugar level of the body. Uncontrolled diabetes can cause hypertension, also known as high blood glucose. Hypertension in the long run can be a cause of heart disease. Therefore, the prediction of diabetes is a major problem in the health sector. Various machine learning methods are already in use for the prediction of diabetes. Traditional methods were first used to address diabetes forecasting, but they take a while to deploy. Therefore, future research has adopted the use of various optimization strategies to solve the issue of diabetes forecasting as well as to boost system selectivity and operating speed. These methods have all proven to be successful in solving different optimization issues and also improve the model significantly. This work proposes a Logistic Regression model by the use of nature-inspired algorithms. We proposed a methodology which uses seven different optimization algorithms instead of gradient descent for linear regression. We implemented a linear regression algorithm from scratch and then used it with different optimization algorithms which improve the overall performance. © 2023 IEEE.

关键词： Blood

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Research on Transformer Fault Voice Print Information processing Based on Wavelet Packet Optimization Transform 3

Research on Transformer Fault Voice Print Information Proces...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Wang, Lili Li, Zibin Li, Jianwu Lin, Wande Ren, Jiyun Yu, Wanhong Zhao, Bingnan Chen, Nan Zhao, Dianzhe Shenyang Institute of Engineering Shenyang110136 China

ISBN: (纸本)9781510664814

In recent years, more and more attention has been paid to the study of transformer voiceprint. Therefore, this paper mainly studies the denoising technology of transformer voiceprint signal in high noise environment, compares the wavelet packet transform and the wavelet transform, respectively, and finds that the de-noising effect of wavelet packet transform is better than that of wavelet transform. The three-layer decomposition of vowels in the high-frequency part is conducive to the subsequent extraction and identification of fault signals. © 2023 SPIE.

关键词： Wavelet analysis

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machine learning based human recognition via robust Features from audio signals 3

Machine Learning based human recognition via robust Features...

引用

3rd IEEE international conference on Artificial Intelligence, ICAI 2023

作者： Sadique, Umar Khan, Muhammad Suleman Anwar, Shahzad Ahmad, Mehran University of Engineering and Technology AI in Healthcare Lab National Centre of AI Peshawar Pakistan University of Engineering and Technology Department of Electrical Engineering Bannu Campus Peshawar Pakistan University of Engineering and Technology Department of Mechatronics Engineering Peshawar Pakistan

ISBN: (纸本)9798350322125

Biometric verification techniques are commissioned throughout the world in different applications. Human voice recognition is one of the biometric techniques. This technique consists of identifying a human from their voice characteristic. This popular and beneficial biometric technique could be employed for identity human, security purposes, and many different applications. Human audio signal recognition consists of two phases i.e., Features Extraction and Classification. The proposed work consists of extracting features through the Mel Frequency Cepstral-Coefficient (MFCC) from the human audio signal, selecting robust features through Principal Component Analysis PCA, and classifying the selected features by comparing seven machine learning and proposed deep learning algorithms. Finally, compare the performance of different algorithms with different percentages of selected features to evaluate the acceptance rate of the correlated features. Support Vector machine SVM shows the best performance with an Accuracy of 99.27% with F1-score and ROC values of 1.00. In the comparison with other methods, the Random Forest and CNN-ANN are 2nd top robust models with an accuracy of 98.7%. Some of the algorithm's accuracy decreased with fewer features, including Naïve Bayes accuracy suddenly decreases to 60% on 20% of total features. The experiment concludes the acceptance rate of correlated features in different ML and DL algorithms are different in speech processing data. © 2023 IEEE.

关键词： learning algorithms

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A Literature Survey on Classification of Electrocardiogram(ECG) Abnormalities 3

A Literature Survey on Classification of Electrocardiogram(E...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Betha, Sashi Kanth Sreya Sri, K. Jyotshna, L. Raji Naga Sai, L. Nikhita, P. Vignan's Institute of Engineering for Women Department of E.C.E Duvvada India Duvvada India

ISBN: (纸本)9798350343632

The ECG signal is used in the field of cardiology to monitor and analyze the activity of the heart. This focuses on development and implementation of advanced machine learning algorithms and signal processing techniques for the automated classification and identification of abnormalities in ECG signals. The literature survey is the study of accuracy and efficiency of ECG analysis, thereby aiding healthcare professionals in the timely diagnosis and treatment of cardiovascular diseases. To achieve this, a comprehensive dataset of ECG recordings is collected and preprocessed to extract relevant data from the ECG signals. Feature extraction techniques such as wavelet-based features, frequency domain, and temporal domain are used. A variety of methods, including neural networks (NN), LDA, SVM, KNN, decision trees, and Bayesian clarifiers, have been employed to organize ECG signals. In order to diagnose arrhythmias, a two-dimensional CNN model is used for ECG signal classification, with the help of CNN's LSTM. Deep learning has recently gained a lot of popularity and been effectively used for the classification of heart disease and arrhythmia, ST segment or classification of heartbeat. A variety of machine learning models, such as RNNs, HRNNs and ensemble techniques are trained and evaluated for their performance in classifying ECG signals into different categories, including normal rhythms and various cardiac arrhythmias. Additionally, anomaly detection algorithms are employed to identify subtle deviations from normal ECG patterns. The literature survey results studied from this research have the potential to enhance patient care by enabling early detection of cardiac abnormalities, leading to more timely interventions and improved clinical outcomes. Furthermore, the methodology explained in this survey can be applied to other areas of medical signal analysis, paving the way for more efficient and reliable diagnostic tools in the field of healthcare. The primary goals of

关键词： Arrhythmia Convolutional neural networks (CNNs) Electrocardiogram Handcrafted-Rule-enhanced Neural Network (HRNN) machine learning algorithms Recurrent neural networks (RNNs) Support Vector machine (SVM)

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Diagnosing Faults of Reciprocating Air Compressor (RAC) Setup Using signal processing Technique and machine learning Approach 3rd

Diagnosing Faults of Reciprocating Air Compressor (RAC) Setu...

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3rd international conference on Mechanical and Energy Technologies, ICMET 2023

作者： Dhakar, Atul Singh, Bhagat Gupta, Pankaj Jaypee University of Engineering and Technology Madhya Pradesh Guna India

ISBN: (纸本)9789819749461

This paper presents a method for identifying RAC faults using acoustic signals obtained from both healthy and unhealthy conditions. The entire procedure is carried out with microphones. Accumulated one healthy and seven unhealthy signals of RAC setup processed using unconventional method of signal processing called "local mean decomposition" (LMD). Additionally, the ‘6’ statistical properties (SP) have been evaluated in order to extract features: mean (US), variance (σs2), root square of mean (Mrms), root amplitude of mean (Mrma), absolute amplitude of mean (Mama), and kurtosis index (Ki). Extracted fault features are classified using lazy learning-based (LLB) classifiers. It has been observed that from different types of LLB classifiers, K-star classifier is quite accurate and has higher accuracy (93.05%) as compared to the other two classifiers accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reciprocating compressors

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