Relevant bibliographies by topics / Multi-Layers Perceptron (MLP)Classifier

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Academic literature on the topic 'Multi-Layers Perceptron (MLP)Classifier'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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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
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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
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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 grove
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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 wi
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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 tw
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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 tw
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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 B
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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 neur
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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 feat
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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 preprocess
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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 c
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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 indic
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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 Immunogl
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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 Immunogl
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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, busine
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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 WE
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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
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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
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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-defecti
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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 pro
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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 cyl
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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 or
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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 net
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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 ga
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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 di
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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
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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 account
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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
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