Academic literature on the topic 'Multi-Layers Perceptron (MLP)Classifier'
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Journal articles on the topic "Multi-Layers Perceptron (MLP)Classifier"
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Narayan, Yogendra. "Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 3339–44. http://dx.doi.org/10.17762/turcomat.v12i2.2393.
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Electroencephalogram (EEG)signals based brain-computer interfacing (BCI) is the current technology trends in the field of rehabilitation robotic. This study compared the performance of support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) classifier with the combination of eight different features as a feature vector. EEG data were acquired from 20 healthy human subjects with predefined protocols. After the EEG signals acquisition, it was pre-processed followed by feature extraction and classification by using SVM MLP and LDA classifiers. The results exhibited that the SVM method was the best approach with 98.8% classification accuracy followed by MLP classifier. Finally, the SVM classifier and Arduino Mega controller was employed for offline controlling of the gripper of the robotic arm prototype. The finding of this study may be useful for online controlling as well as multi-degree of freedom with multi-class EEG dataset.
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Narayan, Yogendra. "Motor-Imagery based EEG Signals Classification using MLP and KNNClassifiers." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 3345–50. http://dx.doi.org/10.17762/turcomat.v12i2.2394.
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The electro encephalo gram (EEG) signals classification playsa major role in developing assistive rehabilitation devices for physically disabled performs. In this context, EEG data were acquired from 20 healthy humans followed by the pre-processing and feature extraction process. After extracting the 12-time domain features, two well-known classifiers namely K-nearest neighbor (KNN) and multi-layer perceptron (MLP) were employed. The fivefold cross-validation approach was utilized for dividing data into training and testing purpose. The results indicated that the performance of MLP classifier was found better than the KNN classifier. MLP classifier achieved 95% classifier accuracy which is the best. The outcome of this study would be very useful for online development of EEG classification model as well as designing the EEG based wheelchair.
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Osa, Priscilla Indira, Anne-Laure Beck, Louis Kleverman, and Antoine Mangin. "Multi-Classifier Pipeline for Olive Groves Detection." Applied Sciences 13, no. 1 (2022): 420. http://dx.doi.org/10.3390/app13010420.
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Pixel-based classification is a complex but well-known process widely used for satellite imagery classification. This paper presents a supervised multi-classifier pipeline that combined multiple Earth Observation (EO) data and different classification approaches to improve specific land cover type identification. The multi-classifier pipeline was tested and applied within the SCO-Live project that aims to use olive tree phenological evolution as a bio-indicator to monitor climate change. To detect and monitor olive trees, we classify satellite images to precisely locate the various olive groves. For that first step we designed a multi-classifier pipeline by the concatenation of a first classifier which uses a temporal Random-Forest model, providing an overall classification, and a second classifier which uses the result from the first classification. IOTA2 process was used in the first classifier, and we compared Multi-layer Perceptron (MLP) and One-class Support Vector Machine (OCSVM) for the second. The multi-classifier pipelines managed to reduce the false positive (FP) rate by approximately 40% using the combination RF/MLP while the RF/OCSVM combination lowered the FP rate by around 13%. Both approaches slightly raised the true positive rate reaching 83.5% and 87.1% for RF/MLP and RF/OCSVM, respectively. The overall results indicated that the combination of two classifiers pipeline improves the performance on detecting the olive groves compared to pipeline using only one classifier.
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Yang, Yingjian, Nanrong Zeng, Ziran Chen, et al. "Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification." Journal of Healthcare Engineering 2023 (November 3, 2023): 1–15. http://dx.doi.org/10.1155/2023/3715603.
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Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier’s performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.
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Camelo, Pedro Henrique Cardoso, and Rafael Lima De Carvalho. "Multilayer Perceptron optimization through Simulated Annealing and Fast Simulated Annealing." Academic Journal on Computing, Engineering and Applied Mathematics 1, no. 2 (2020): 28–31. http://dx.doi.org/10.20873/ajceam.v1i2.9474.
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The Multilayer Perceptron (MLP) is a classic and widely used neural network model in machine learning applications. As the majority of classifiers, MLPs need well-defined parameters to produce optimized results. Generally, machine learning engineers use grid search to optimize the hyper-parameters of the models, which requires to re-train the models. In this work, we show a computational experiment using metaheuristics Simulated Annealing and Fast Simulated Annealing for optimization of MLPs in order to optimize the hyper-parameters. In the reported experiment, the model is used to optimize two parameters: the configuration of the neural network layers and its neuron weights. The experiment compares the best MLPs produced by the SA and FastSA using the accuracy and classifier complexity as comparison measures. The MLPs are optimized in order to produce a classifier for the MNIST database. The experiment showed that FastSA has produced a better MLP, using less computational time and less fitness evaluations.
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Henrique Cardoso Camelo, Pedro, and Rafael Lima De Carvalho. "Multilayer Perceptron optimization through Simulated Annealing and Fast Simulated Annealing." Academic Journal on Computing, Engineering and Applied Mathematics 1, no. 2 (2020): 28–31. http://dx.doi.org/10.20873/uft.2675-3588.2020.v1n2.p28-31.
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The Multilayer Perceptron (MLP) is a classic and widely used neural network model in machine learning applications. As the majority of classifiers, MLPs need well-defined parameters to produce optimized results. Generally, machine learning engineers use grid search to optimize the hyper-parameters of the models, which requires to re-train the models. In this work, we show a computational experiment using metaheuristics Simulated Annealing and Fast Simulated Annealing for optimization of MLPs in order to optimize the hyper-parameters. In the reported experiment, the model is used to optimize two parameters: the configuration of the neural network layers and its neuron weights. The experiment compares the best MLPs produced by the SA and FastSA using the accuracy and classifier complexity as comparison measures. The MLPs are optimized in order to produce a classifier for the MNIST database. The experiment showed that FastSA has produced a better MLP, using less computational time and less fitness evaluations.
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Hussein, Ali Bashar, Raid Rafi Omar Al-Nima, and Tingting Han. "Stammering Algorithm with Adapted Multi-Layer Perceptron." Jurnal Kejuruteraan 36, no. 5 (2024): 1921–33. http://dx.doi.org/10.17576/jkukm-2024-36(5)-12.
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Stuttering (or stammering) is a common speech disorder that may continue until adulthood, if not treated in its early stages. In this study, we suggested an efficient algorithm to perform stammering corrections (anti-stammering). This algorithm includes an effective feature extraction approach and an adapted classifier. We introduced Enhanced 1D Local Binary Patterns (EOLBP) for the extraction of features and adapted a classifier of Multi-Layer Perceptron (MLP) neural network for regression. This paper uses a database that involves speech signals with stammering, it can be called the Fluency Bank (FB). The result reveals that the proposed anti-stammering algorithm obtains promising achievement, where a high accuracy of 97.22% is attained.
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Rezaeipanah, Amin, Rahmad Syah, Siswi Wulandari, and A. Arbansyah. "Design of Ensemble Classifier Model Based on MLP Neural Network For Breast Cancer Diagnosis." Inteligencia Artificial 24, no. 67 (2021): 147–56. http://dx.doi.org/10.4114/intartif.vol24iss67pp147-156.
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Nowadays, breast cancer is one of the leading causes of death women in the worldwide. If breast cancer is detected at the beginning stage, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of this cancer, however, efforts are still ongoing given the importance of the problem. Artificial Neural Networks (ANN) have been established as some of the most dominant machine learning algorithms, where they are very popular for prediction and classification work. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method is split into two stages, parameters optimization and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimized with an Evolutionary Algorithm (EA) for maximize the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN is applied to classify the patient with optimized parameters. Our proposed IEC-MLP method which can not only help to reduce the complexity of MLP-NN and effectively selection the optimal feature subset, but it can also obtain the minimum misclassification cost. The classification results were evaluated using the IEC-MLP for different breast cancer datasets and the prediction results obtained were very promising (98.74% accuracy on the WBCD dataset). Meanwhile, the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP could also be applied to other cancer diagnosis.
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Ranjeeth, Sama, and Thamarai Pugazhendhi Latchoumi. "Predicting Kids Malnutrition Using Multilayer Perceptron with Stochastic Gradient Descent." Revue d'Intelligence Artificielle 34, no. 5 (2020): 631–36. http://dx.doi.org/10.18280/ria.340514.
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The capability of predicting malnutrition kids is highly beneficial to take remedial actions on kids who are under 5 year’s age. In this article, Kid’s malnutrition predictive model is created and tested with our own collected dataset. We find the issues of kids malnutrition by the use of Machine Learning (ML) models. From ML-models, a multi-layer perceptron is used to classify the data neatly. Optimizing technique stochastic gradient descent (SGD) and Multilayer Perceptron (MLP) classifier methods are integrated to classify the data more effectively. To select the best features, from the feature selection (FS) technique filter-based method used. After selecting the best features, selected features are pass to the classifier model then the model will classify the data. Results with the MLP-SGD classifier were good than the other classifiers but after feature selection, the performance of the model was increased more. It will help in improving the analysis of malnutrition kid’s data. The sample data are collected from parents who are having kids less than five years of age at Repalle town, Andhra Pradesh, India.
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Yudhistira, Galih, Pika Aliya Widiastuti, Rahyuni Rahyuni, Tri Hastono, and Eko Harry Pratisto. "Multi-Layer Perceptron Model for Dota 2 Game Results from UCI Using MLP Classifier." APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL 2, no. 2 (2023): 67–72. http://dx.doi.org/10.31316/astro.v2i2.5797.
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Dota 2 is a genre game Moba in the PC (Personal Computer) system battle arena game online (online) with multiplayer ( bringing together 2 players in 1 machine ). Game Dota 2 consists of 2 opposing teams To get the victory, every team has 5 players who can choose hero 1 from 121 different heroes. Study This discusses the use of the Multi-Layer Perceptron (MLP) model to predict the results Dota 2 game. The author uses the UCI dataset containing historical data of Dota 2 matches, processed and trained with the MLP model using MLPClassifier from the scikit learn Python library. The data preprocessing process includes normalization features and handling of missing data. Training involves hyperparameter selection and validation cross To prevent overfitting. Although the MLP model is successful in predicting results with accuracy high, the author takes notes room For improvement, like additional features or the use of more models complex. In research, This obtained results with Accuracy Train results: 68.06%, Accuracy Test: 58.00%, Accuracy Precision: 58.53%, Accuracy Recall: 73.50%, Accuracy f1: 63.39%.
Book chapters on the topic "Multi-Layers Perceptron (MLP)Classifier"
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Palliyil Sreekumar, Sreelekshmi, Rohini Palanisamy, and Ramakrishnan Swaminathan. "An Approach to Differentiate Cell Painted ER and Cytoplasm Using Zernike Moment Descriptor and Multilayer Perceptron." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220724.
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Differentiation of cell organelle characteristics from microscopic images is a challenging task due to its intricate structural details. In this work, an attempt has been made to categorize Endoplasmic Reticulum (ER) and cytoplasm using orthogonal Zernike moments and Multilayer Perceptron (MLP). For this, Cell painted public source dataset comprising of ER and cytoplasm are considered. Zernike moments for different orders and repetition of the azimuthal angle are extracted to characterize the shape features. The extracted features are validated using MLP classifier for differentiating ER and cytoplasm. The prediction accuracy for variations in the number of hidden layers are evaluated. The experimental results show that the accuracy varies as the size of hidden layer increases. The extracted features with MLP achieved an accuracy of 85% with a hidden layer size of 5. The receiver operating characteristic curve (ROC) demonstrates the distinguishing power of MLP classifier with AUC=0.92. This study suggests that the proposed framework can be employed for analyzing the morphological variations of cell organelles due to chemical perturbations, genome variations and cytotoxic effects using the combination of Zernike shape descriptor and MLP. The orthogonality property of Zernike shape descriptor provides independent unique features which reduce redundancy and improve prediction accuracy for large datasets.
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Kumar, C. Sathish, B. Sathees Kumar, Gnaneswari Gnanaguru, V. Jayalakshmi, S. Suman Rajest, and Biswaranjan Senapati. "Augmenting Chronic Kidney Disease Diagnosis With Support Vector Machines for Improved Classifier Accuracy." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5946-4.ch024.
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Mitigating chronic kidney disease poses a substantial challenge to the healthcare community. This study assesses diverse classification algorithms, encompassing NaiveBayes, multi-layer perceptron, and support vector machine. The analysis involves scrutinizing the chronic kidney disease dataset from the UCI machine learning repository. Techniques like replacing missing values, unsupervised discretization, and normalization are employed for precision enhancement. The empirical results of the classification models are evaluated for accuracy and computational time. The conclusive observation indicates that the support vector machine performs notably better than all other classification methods, with a 76% classifier accuracy which is better than classifiers such as MLP and NB. The lack of application of those feature selection methods to the dataset is a drawback of this study.
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Alonso, Jose M., Ciro Castiello, Marco Lucarelli, and Corrado Mencar. "Modeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support." In Data Mining. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch054.
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Decision support systems in Medicine must be easily comprehensible, both for physicians and patients. In this chapter, the authors describe how the fuzzy modeling methodology called HILK (Highly Interpretable Linguistic Knowledge) can be applied for building highly interpretable fuzzy rule-based classifiers (FRBCs) able to provide medical decision support. As a proof of concept, they describe the case study of a real-world scenario concerning the development of an interpretable FRBC that can be used to predict the evolution of the end-stage renal disease (ESRD) in subjects affected by Immunoglobin A Nephropathy (IgAN). The designed classifier provides users with a number of rules which are easy to read and understand. The rules classify the prognosis of ESRD evolution in IgAN-affected subjects by distinguishing three classes (short, medium, long). Experimental results show that the fuzzy classifier is capable of satisfactory accuracy results – in comparison with Multi-Layer Perceptron (MLP) neural networks – and high interpretability of the knowledge base.
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Alonso, Jose M., Ciro Castiello, Marco Lucarelli, and Corrado Mencar. "Modeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support." In Medical Applications of Intelligent Data Analysis. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1803-9.ch017.
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Decision support systems in Medicine must be easily comprehensible, both for physicians and patients. In this chapter, the authors describe how the fuzzy modeling methodology called HILK (Highly Interpretable Linguistic Knowledge) can be applied for building highly interpretable fuzzy rule-based classifiers (FRBCs) able to provide medical decision support. As a proof of concept, they describe the case study of a real-world scenario concerning the development of an interpretable FRBC that can be used to predict the evolution of the end-stage renal disease (ESRD) in subjects affected by Immunoglobin A Nephropathy (IgAN). The designed classifier provides users with a number of rules which are easy to read and understand. The rules classify the prognosis of ESRD evolution in IgAN-affected subjects by distinguishing three classes (short, medium, long). Experimental results show that the fuzzy classifier is capable of satisfactory accuracy results – in comparison with Multi-Layer Perceptron (MLP) neural networks – and high interpretability of the knowledge base.
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Veziroğlu, Merve, Erkan Eziroğlu, and İhsan Ömür Bucak. "PERFORMANCE COMPARISON BETWEEN NAIVE BAYES AND MACHINE LEARNING ALGORITHMS FOR NEWS CLASSIFICATION." In Bayesian Inference - Recent Trends. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1002778.
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The surge in digital content has fueled the need for automated text classification methods, particularly in news categorization using natural language processing (NLP). This work introduces a Python-based news classification system, focusing on Naive Bayes algorithms for sorting news headlines into predefined categories. Naive Bayes is favored for its simplicity and effectiveness in text classification. Our objective includes exploring the creation of a news classification system and evaluating various Naive Bayes algorithms. The dataset comprises BBC News headlines spanning technology, business, sports, entertainment, and politics. Analyzing category distribution and headline length provided dataset insights. Data preprocessing involved text cleaning, stop word removal, and feature extraction with Count Vectorization to convert text into machine-readable numerical data. Four Naive Bayes variants were evaluated: Gaussian, Multinomial, Complement, and Bernoulli. Performance metrics such as accuracy, precision, recall, and F1 score were employed, and Naive Bayes algorithms were compared to other classifiers like Logistic Regression, Random Forest, Linear Support Vector Classification (SVC), Multi-Layer Perceptron (MLP) Classifier, Decision Trees, and K-Nearest Neighbors. The MLP Classifier achieved the highest accuracy, underscoring its effectiveness, while Multinomial and Complement Naive Bayes proved robust in news classification. Effective data preprocessing played a pivotal role in accurate categorization. This work contributes insights into Naive Bayes algorithm performance in news classification, benefiting NLP and news categorization systems.
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Balogun, Jeremiah Ademola, Adanze O. Asinobi, Olawale Olaniyi, Samuel Ademola Adegoke, Florence Alaba Oladeji, and Peter Adebayo Idowu. "Ensemble Model for the Risk of Anemia in Pediatric Patients With Sickle Cell Disorder." In Research Anthology on Pediatric and Adolescent Medicine. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5360-5.ch002.
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Anemia is a major cause of morbidity and mortality of SCD patients in many parts of the world with the burden much higher in Sub Saharan Africa. This study developed an ensemble of machine learning algorithm for the prediction of the risk of anemia in pediatric SCD patients. Data for this study was collected from 115 pediatric SCD outpatients receiving treatment at a tertiary hospital in South-Western Nigeria. This study adopted a stack-ensemble model composed of deep neural network (DNN), multi-layer perceptron (MLP), and support vector machines (SVM) as base and meta-classifiers using the WEKA software. The ensemble models were compared following the stack-ensemble developed using SVM as a meta-classifier had the best performance with an accuracy of 72.7%. The study concluded that information about socio-demographic and clinical data can be used to assess the risk of anemia among SCD patients.
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Wu, Yuhan, and Yiqin Bao. "Classification of Odor Drift Data Based on Several Machine Learning Algorithms." In Fuzzy Systems and Data Mining IX. IOS Press, 2023. http://dx.doi.org/10.3233/faia231045.
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Based on the classification and recognition algorithm of machine learning, this paper analyzes and researches the odor drift data set. First of all, data visualization is used to effectively master the data distribution law, coherence, outlier noise points and other information of the data set. According to the situation, the data is normalized and dimensionality reduction preprocessing, and the training set and test set are divided. KNN model, decision tree model, random forest classifier model and MLP multi-layer perceptron model were used to test and compare the data sets. The test results show that the performance of random forest model for odor drift data classification is relatively good, up to 95%, which can be used in practice.
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R. L., Priya, and S. Vinila Jinny. "Comparison Analysis of Prediction Model for Respiratory Diseases." In Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4703-8.ch004.
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Millions of people around the world have one or many respiratory-related illnesses. Many chronic respiratory diseases like asthma, COPD, pneumonia, respiratory distress, etc. are considered to be a significant public health burden. To reduce the mortality rate, it is better to perform early prediction of respiratory disorders and treat them accordingly. To build an efficient prediction model for various types of respiratory diseases, machine learning approaches are used. The proposed methodology builds classifier model using supervised learning algorithms like random forest, decision tree, and multi-layer perceptron neural network (MLP-NN) for the detection of different respiratory diseases of ICU admitted patients. It achieves accuracy of nearly 99% by various machine learning approaches.
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Panda, Mrutyunjaya, and Ahmad Taher Azar. "Hybrid Multi-Objective Grey Wolf Search Optimizer and Machine Learning Approach for Software Bug Prediction." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5788-4.ch013.
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Software bugs (or malfunctions) pose a serious threat to software developers with many known and unknown bugs that may be vulnerable to computer systems, demanding new methods, analysis, and techniques for efficient bug detection and repair of new unseen programs at a later stage. This chapter uses evolutionary grey wolf (GW) search optimization as a feature selection technique to improve classifier efficiency. It is also envisaged that software error detection would consider the nature of the error when repairing it for remedial action instead of simply finding it either faulty or non-defective. To address this problem, the authors use bug severity multi-class classification to build an efficient and robust prediction model using multilayer perceptron (MLP), logistic regression (LR), and random forest (RF) for bug severity classification. Both tests are performed on two software error datasets, namely Ant 1.7 and Tomcat.
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Sayoud, Halim, and Siham Ouamour. "Speaker Discrimination on Broadcast News and Telephonic Calls Based on New Fusion Techniques." In Innovations in Mobile Multimedia Communications and Applications. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-563-6.ch017.
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This chapter describes a new Speaker Discrimination System (SDS), which is a part of an overall project called Audio Documents Indexing based on a Speaker Discrimination System (ADISDS). Speaker discrimination consists in checking whether two speech segments come from the same speaker or not. This research domain presents an important field in biometry, since the voice remains an important feature used at distance (via telephone). However, although some discriminative classifiers do exist nowadays, their performances are not enough sufficient for short speech segments. This issue led us to propose an efficient fusion between such classifiers in order to enhance the discriminative performance. This fusion is obtained, by using three different techniques: a serial fusion, parallel fusion and serial-parallel fusion. Also, two classifiers have been chosen for the evaluation: a mono-gaussian statistical classifier and a Multi Layer Perceptron (MLP). Several experiments of speaker discrimination are conducted on different databases: Hub4 Broadcast-News and telephonic calls. Results show that the fusion has efficiently improved the scores obtained by each approach alone. So, for instance, the authors got an Equal Error Rate (EER) of about 7% on a subset of Hub4 Broadcast-News database, with short segments of 4 seconds, and an EER of about 4% on telephonic speech, with medium segments of 10 seconds.
Conference papers on the topic "Multi-Layers Perceptron (MLP)Classifier"
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Devi, R. Manjula, P. Keerthika, P. Suresh, et al. "Twitter Sentiment Analysis using Collaborative Multi Layer Perceptron(MLP) Classifier." In 2023 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2023. http://dx.doi.org/10.1109/iccci56745.2023.10128430.
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R, Sushmitha Saro, Jaya Suriya B, and Rajakumari R. "Comprehensive Speech Emotion Recognition System Employing Multi-Layer Perceptron (MLP) Classifier and libRosa Feature Extraction." In 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA). IEEE, 2023. http://dx.doi.org/10.1109/icscna58489.2023.10370394.
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Geraei, Hosna, Essam Seddik, Ghabi Neame, Elliot (Yixin) Huangfu, and Saeid Habibi. "Machine Learning-Based Fault Detection and Diagnosis of Internal Combustion Engines Using an Optical Crank Angle Encoder." In ASME 2022 ICE Forward Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icef2022-88851.
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Abstract Fault Detection and Diagnosis (FDD) in internal combustion engines is an important tool for better performance, safety, reliability, and instrument to reduce maintenance costs. Early detection of engine faults can help avoid abnormal event progression to failure. This study is carried out to develop two FDD algorithms to detect and diagnose internal combustion engine faults using an optical crank angle encoder. Experiments were carried out on a 2018 Ford Gen 3, 5.0L, V8, Coyote engine to achieve these goals. The engine head was modified to access the combustion chamber of specific cylinders for in-cylinder pressure measurement and, subsequently, combustion analysis. During this project, three engine faults were introduced: EGR valve failure, cylinder leakage, and spark plug degradation. In the first method, Fast Fourier Transform (FFT) is applied to the data collected using the optical crank angle encoder. FFT converts the crank angle domain data to the frequency domain. Then, the data dimension is reduced using Principal Component Analysis (PCA). The dataset with reduced dimensions is used as Multi-layer Perceptron (MLP) inputs. 10-fold cross-validation is used to determine the number of hidden layers in the MLP. The MLP model detects and diagnoses severities of cylinder leaks and EGR faults with a relatively high success rate (92%). The second method developed a classification model using the Random Forest (RF) classifier and Curve Descriptive (CD) Features. The performance of the MLP model and the Curve Descriptive features with Random Forest (CD-RF) models for detecting and diagnosing misfire faults are compared. Results show that the MLP model and CD-RF model accuracy for classifying misfire faults are 86.67% and 88,89%, respectively.
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Aydemir, Gürkan. "Deep Learning Based Spectrum Compression Algorithm for Rotating Machinery Condition Monitoring." In ASME 2018 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/smasis2018-8137.
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In the new data intensive world, predictive maintenance has become a central issue for the modern industrial plants. Monitoring of electric machinery is one of the most important challenges in predictive maintenance. Adaptive manufacturing processes/plants may be possible through the monitored conditions. In this respect, several attempts have been made to utilize deep learning algorithms for rotating machinery fault detection and diagnosis. Among them, deep autoencoders are very popular, because of their denoising effect. They are also implemented in electric machinery fault diagnostics in order to obtain lower order representation of signals. However, none of these efforts regard the autoencoders as compression units. Bearing in mind that spectra of vibration and current signals that are collected from electric machinery are critical instruments for detection and diagnosis of their faults, we propose that deep stacked autoencoder can be utilized as spectrum compression units. The performance of the proposed strategy are assessed using a bearing data set in three ways: (1)Rule-based classifiers are implemented on raw and compressed-decompressed spectrum and their performance are compared. (2) It is shown that the several machine learning classifiers such as support vector machines, artificial neural networks and k-nearest neighbour classifiers on compressed-decompressed spectrum achieves the performance of them on raw data. (3) A multi-layer perceptron (MLP) classifier is implemented on the low dimensional representation and it is demonstrated that the strategy of employing the same autoencoder as pretraining of feature extraction module cannot outperform the performance of this MLP classifier.
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Li, Lingqi, Wei Cheng, Kazuhiko Tsukada, and Koichi Hanasaki. "Flaw Classification by Using Artificial Neural Network and Wavelet." In ASME/JSME 2004 Pressure Vessels and Piping Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/pvp2004-2815.
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This paper presents a methodology to 2-D flaw-shape recognition by combining a neural network and the wavelet feature extractor. This approach consists of three stages. First, the 2-D pattern of an object is retrieved from image and then transformed to complex contour, which is described by the coordinates of its shape. Then, feature extraction is performed to this contour representation. Fourier descriptor (FD), principal component analysis (PCA) and wavelet descriptor (WD) are employed in this stage, and their performances are compared and discussed. In the third stage, artificial neural networks, including two different types of multi-layer perceptron (MLP) and Kohonen self-organizing network, are used as the classifier based on the feature sets extracted in the second stage. The numerical experiments performed on the recognition of simulated shapes demonstrate the superiority of the WD feature extractor (both used for MLP and Kohonen network classifiers) to the other two: PCA and FD, especially when the raw data have poor signal-to-noise ratio (SNR). The application to the real ultrasonic C-scan image flaw-shape classification shows the effectiveness of the proposed approach to the field of PVP.
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Tan, Jie Ying, and Andy Sai Kit Chow. "Sentiment Analysis on Game Reviews: A Comparative Study of Machine Learning Approaches." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1023.
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Sentiment analysis is one of the major topics of natural language processing which is used to determine whether data is positive, negative or neutral. It is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback to understand their customers’ needs. This paper explores various machine learning algorithms including Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Support Vector Classifier (SVC), Multi-layer Perceptron Classifier (MLP) and Extreme Gradient Boosting Classifier (XGB) to build sentiment analysis models tailored for the gaming domain to classify reviews into positive, negative and neutral. The models were trained on game reviews obtained from Metacritic and Steam. Various data preprocessing and model optimization techniques have been employed and the performance of the models were evaluated and compared. SVC has been determined as the best-performing model among all the models. Keywords: Sentiment Analysis, Natural Language Processing, Machine Learning, Support Vector Machine, Game Reviews
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Bandeira, Jonathan da Silva, and Roberta Andrade de Araújo Fagundes. "Enhancing Alzheimer’s Disease Diagnosis: Insights from MLP and 1D CNN Models." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2025. https://doi.org/10.5753/sbsi.2025.246492.
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Context: Alzheimer’s Disease (AD) is a complex neurodegenerative disorder that requires early diagnosis to improve patient outcomes. Recent advances in computational intelligence have sparked interest in leveraging machine learning to enhance diagnostic accuracy and efficiency. These innovations are crucial for transforming decision-making within Information Systems in clinical settings. Problem: Traditional methods like PET-scans and cerebrospinal fluid collection are highly accurate but costly and invasive, limiting accessibility. Developing data-driven, non-invasive solutions that retain diagnostic accuracy while handling complex biomedical data, such as plasma protein concentrations, remains a challenge. Solution: This study utilizes neural networks, specifically Multi-Layer Perceptron (MLP) and One-Dimensional Convolutional Neural Network (1D CNN). Preprocessing included Recursive Feature Elimination (RFE) for feature selection and Synthetic Minority Oversampling Technique (SMOTE) for data augmentation, addressing class imbalance. SI Theory: Grounded in Complexity Theory, the study examines how machine learning models can enhance data-driven medical systems by efficiently managing critical, highly sensitive datasets. Method: An experimental quantitative approach was used to evaluate binary and multiclass classifiers on a dataset with 120 protein features from 259 patients. Summary of Results: The MLP exhibited strong performance in specific subsets, achieving superior metrics in the binary classification after feature selection and data augmentation. Meanwhile, the 1D CNN excelled in multiclass classification, leveraging its convolutional layers to extract critical features from subtle protein variations, improving accuracy and robustness. Contributions and Impact on IS Field: This research enhances medical information systems by proposing machine learning models that can be integrated for accurate diagnostics, supporting clinical decision-making and advancing healthcare practices.
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Magdoom, K. N., Thomas H. Mareci, and Malisa Sarntinoranont. "Segmentation of Rat Brain MR Images Using Artificial Neural Network Classifier." In ASME 2013 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/sbc2013-14399.
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Recently MR image based computational models are being developed to assist targeted drug delivery in the brain by helping determine appropriate catheter position, drug dose among others to achieve the desired drug distribution [1–3]. Such a planning might be important to prevent damaging healthier tissues because many of the drugs (e.g. chemotherapeutic agents) are usually toxic and needs to be concentrated in specific regions of interest (e.g. tumor). However, for the image based model to make accurate predictions, it is important to segment the image and assign appropriate tissue properties such as hydraulic conductivity which are known to vary significantly within the brain. For example, it has been experimentally found that drugs injected into brain parenchyma get preferentially transported along the white matter tracts compared to the gray matter regions [4]. Segmenting MR images is a challenging task since the pixel intensities between different regions often overlap, hence traditional approaches based on thresholds might not provide reliable results. In this study, we used multi-layered perceptron (MLP) neural network to segment rat brain MR images into 3 different regions namely white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF).
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Ghassemi, Payam, Kaige Zhu, and Souma Chowdhury. "Optimal Surrogate and Neural Network Modeling for Day-Ahead Forecasting of the Hourly Energy Consumption of University Buildings." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68350.
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This paper presents the development and evaluation of Artificial Neural Networks (ANN) based models and optimally selected surrogate models to provide the day-ahead forecast of the hourly-averaged energy load of buildings, by relating it to eight weather parameters as well as the hour of the day. Although ANN and other surrogate models have been used to predict building energy loads in the past, there is a limited understanding of what type of model prescriptions impact their performance as well as how un-recorded impact factors (e.g., human behavior and building repair work) should be accounted for. Here, the recorded energy data of three university buildings, from 9/2013–12/2015, is cleaned and synchronized with the local weather data. The data is then classified into eight classes; weekends and weekdays of Fall/Winter/Spring/Summer semesters. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) NNs are explored. Differing number of hidden layers and transfer function choices are also explored, leading to the choice of the hyperbolic-tangent-sigmoid transfer function and 60 hidden layers. Similarly, an automated surrogate modeling framework is used to select the best models from among a pool of Kriging, RBF, and SVR models. A baseline concept, that uses energy information from the previous day as an added input to the ANN, helps to account for otherwise unrecorded recent changes in the building behavior, leading to improvement in fidelity of up to 30%.
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Alozie, Ogechukwu, Yi-Guang Li, Pericles Pilidis, et al. "An Integrated Principal Component Analysis, Artificial Neural Network and Gas Path Analysis Approach for Multi-Component Fault Diagnostics of Gas Turbine Engines." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15740.
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Abstract Gas path diagnostics is a key aspect of the engine health monitoring (EHM) process that aims to detect, identify and predict engine component faults, using information from installed sensors, in order to guide maintenance action, maintain engine efficiency and prevent catastrophic failures. To achieve high prediction accuracies, current data-derived diagnostic models tend to be engine specific while the model-based methods are known to be time-consuming, especially for complex engine configurations. This paper proposes an integrated approach for accurate and accelerated isolation and prediction of multiple-degraded gas turbine component faults that comprises 3 steps — feature extraction using the Principal Component Analysis (PCA), machine learning classification with a multi-layer perceptron, artificial neural network (MLP-ANN) and model-based fault prediction via the non-linear Gas Path Analysis (GPA) technique. In this hybrid approach, the PCA first transforms the measurement fault signature into a fault-feature domain, which becomes an input to the multi-label ANN classifier used to isolate the potential faulty components. The non-linear GPA finally quantifies the magnitude of degradation that produced the recorded fault signature. Once trained and validated, the PCA-ANN model is deployed as part of the data processing mechanism prior to the actual GPA calculation. This method was assessed and validated using the thermodynamic performance model of a 2-shaft, high-bypass ratio, turbofan engine. For training and testing the PCA-ANN classifier, a total of 28,000 final samples for 14 measurement parameters, each averaged from 10 data points with Gaussian noise of zero mean and unit standard deviation, and implanted with single-, double- and triple-component fault cases of various magnitude, were generated by steady-state performance simulation of the engine model at its reference operating condition. Correlation analysis of this data set revealed the optimum sensor subset to be used for multi-component diagnostics. A quantitative analysis of the PCA-ANN fault isolation on the test set produced a classification accuracy of 96.6% and performed better on all metrics, compared to other multi-label classification algorithms. Finally, the proposed integrated approach achieved an average of 94.35% reduction in processing time, when compared to the conventional non-linear GPA by component-fault-cases (CFCs), while predicting implanted faults to the same accuracy.