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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%.
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Singh, Pawan Kumar, Supratim Das, Ram Sarkar, and Mita Nasipuri. "Line Parameter based Word-Level Indic Script Identification System." International Journal of Computer Vision and Image Processing 6, no. 2 (2016): 18–41. http://dx.doi.org/10.4018/ijcvip.2016070102.
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In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts. Since Optical Character Recognition (OCR) engines are usually script-dependent, automatic text recognition in multi-script environment requires a pre-processing module that helps identifying the scripts before processing the same through the respective OCR engine. The work becomes more challenging when it deals with handwritten document which is still a less explored research area. In this paper, a line parameter based approach is presented to identify the handwritten scripts written in eight popular scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Manipuri, Oriya, Urdu, and Roman. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentations are performed at word-level using multiple classifiers on a dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%. The performance of the present technique is also compared with those of other state-of-the-art script identification methods on the same database. A combination of Hough transform (HT) and Distance transform (DT) is used to extract the directional spatial features based on the line parameter. Experimentation are performed at word-level on a total dataset of 12000 handwritten word images and Multi Layer Perceptron (MLP) classifier is found to be the best performing classifier showing an identification accuracy of 95.28%.
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Karen, Charly Veigas, Srilekha Regulagadda Durga, and Arun Kokatnoor Sujatha. "Optimized Stacking Ensemble (OSE) for Credit Card Fraud Detection using Synthetic Minority Oversampling Model." Indian Journal of Science and Technology 14, no. 32 (2021): 2607–15. https://doi.org/10.17485/IJST/v14i32.807.
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Abstract <strong>Objectives:</strong> Credit fraud is a global threat to financial institutions due to specific challenges like imbalanced datasets and hidden patterns in real-life scenarios. The objective of this study is to propose a model that effectively identifies fraudulent transactions. <strong>Methods:</strong> Methods such as Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN) that artificially generate synthetic data are used in this paper to approximate the distribution of data among the two classes in the original dataset. After balancing the dataset, the individual models Multi-Layer Perceptron (MLP), k- Nearest Neighbors algorithm (kNN) and Support Vector Machine (SVM) are trained on the augmented dataset to establish an initial improvement at the data level. These base-classifiers are further incorporated into the Optimized Stacked Ensemble (OSE) learning process to fit the meta-classifier which creates an effective predictive model for fraud detection. All base-classifiers and the final Optimized Stacked Ensemble (OSE) have been implemented to critically assess and evaluate their performances.<strong> Findings:</strong> Empirical results obtained in this paper show that the quality of the final dataset is considerably improved when Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN) are used as oversampling algorithms. The Multi-Layer Perceptron model showed an increase of 10% in the F1 Score while kNN and SVM showed an increase of 3% each. The optimized model is built using a Stacking Classifier that combines the GAN-improved Multi-Perceptron Model with the other standard classification models such as KNN and SVM. This ensemble outperforms the existing enhanced Multi-Layer Perceptron with near-perfect accuracy (99.86%) and an increase of 16% in F1 Score, resulting in an effective fraud detection mechanism. <strong>Novelty:</strong> For the current dataset, the Optimized Stacked Ensemble model shows an increase of 16% in F1 Score as compared to the existing Multi-Perceptron model. <strong>Keywords:</strong> Ensemble; Credit Card; Fraud Detection; GAN; SMOTE; MLP
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Ahmed, Sheeraz, Zahoor Ali Khan, Syed Muhammad Mohsin, et al. "Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron." Future Internet 15, no. 2 (2023): 76. http://dx.doi.org/10.3390/fi15020076.
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Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naïve Bayes, Decision Stump, Logistic Model Tree, Naïve Bayes Updateable, Naïve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.
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Tavares, Ofelia Cizela da Costa, and Abdullah Zainal Abidin. "ARTIFICIAL NEURAL NETWORK MULTI-LAYER PERCEPTRON FOR DIAGNOSIS OF DIABETES MELLITUS." JIKO (Jurnal Informatika dan Komputer) 7, no. 1 (2024): 19–23. http://dx.doi.org/10.33387/jiko.v7i1.7743.
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Diabetes Mellitus is a disease caused by an unhealthy lifestyle, so blood sugar is not controlled, causing complications. This disease is one of the most dangerous diseases in the world. Approximately 422 million people worldwide have diabetes, the majority living in low- and middle-income countries, and 1.5 million deaths are caused by diabetes each year. The number of cases and prevalence of diabetes have continued to increase over the last few decades. Artificial Neural Networks are a part of machine learning that can solve various problems. One of them is in terms of disease diagnosis. MLP has the advantage that learning is done repeatedly to create a durable, consistent system that works well. This research aims to implement the Multi-Layer Perceptron Artificial Neural Network method for diagnosing diabetes mellitus and then evaluating the MLP by analyzing precision, recall, f1 score, and calculating accuracy. Next, it is validated with k-fold cross-validation. In the experiment in this study, several scenarios were used, and the best scenario was obtained when using eight input layers, seven hidden layers, one output layer, and 5000 iterations. The experiment results showed that the multi-layer perceptron successfully classified diabetics and non-diabetics by percentage. Precision 77.24%, Recall 72.58%, F1 Score 76.86%, accuracy 75%, and average accuracy 78.01%.
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Nezami, Somayeh, Ehsan Khoramshahi, Olli Nevalainen, Ilkka Pölönen, and Eija Honkavaara. "Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks." Remote Sensing 12, no. 7 (2020): 1070. http://dx.doi.org/10.3390/rs12071070.
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Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.
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Kalyani, S., and KS Swarup. "Classification of Static Security Status Using Multi-Class Support Vector Machines." Journal of Engineering Research [TJER] 9, no. 1 (2012): 21. http://dx.doi.org/10.24200/tjer.vol9iss1pp21-30.
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This paper presents a Multi-class Support Vector Machine (SVM) based Pattern Recognition (PR) approach for static security assessment in power systems. The multi-class SVM classifier design is based on the calculation of a numeric index called the static security index. The proposed multi-class SVM based pattern recognition approach is tested on IEEE 57 Bus, 118 Bus and 300 Bus benchmark systems. The simulation results of the SVM classifier are compared to a Multilayer Perceptron (MLP) network and the Method of Least Squares (MLS). The SVM classifier was found to give high classification accuracy and a smaller misclassification rate compared to the other classifier techniques.
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Zhang, Aizhu, Genyun Sun, Ping Ma, et al. "Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles." Remote Sensing 11, no. 8 (2019): 952. http://dx.doi.org/10.3390/rs11080952.
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Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.
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Kumar, N. Komal, R. Lakshmi Tulasi, and Vigneswari D. "An ensemble multi-model technique for predicting chronic kidney disease." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1321–26. https://doi.org/10.11591/ijece.v9i2.pp1321-1326.
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Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learning for predicting Chronic Kidney Disease (CKD). The proposed hybrid classifier is compared with the machine learning classifiers such as Support Vector Machine (SVM), Decision Tree (DT), C4.5, Particle Swarm Optimized Multi Layer Perceptron (PSO-MLP). The proposed classifier accurately predicts the occurrences of kidney disease by analysis various medical factors. The work comprises of two stages, the first stage consists of obtaining weak decision tree classifiers from C4.5 and in the second stage, the weak classifiers are added to the weighted sum to represent the final output for improved performance of the classifier.
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Jondri, Jondri, Indwiarti Indwiarti, and Dyas Puspandari. "Retweet Prediction Using Multi-Layer Perceptron Optimized by The Swarm Intelligence Algorithm." Jurnal Online Informatika 8, no. 2 (2023): 252–60. http://dx.doi.org/10.15575/join.v8i2.1193.
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Retweets are a way to spread information on Twitter. A tweet is affected by several features which determine whether a tweet will be retweeted or not. In this research, we discuss the features that influence the spread of a tweet. These features are user-based, time-based and content-based. User-based features are related to the user who tweeted, time-based features are related to when the tweet was uploaded, while content-based features are features related to the content of the tweet. The classifier used to predict whether a tweet will be retweeted is Multi Layer Perceptron (MLP) and MLP which is optimized by the swarm intelligence algorithm. In this research, data from Indonesian Twitter users with the hashtag FIFA U-20 was used. The results of this research show that the most influential feature in determining whether a tweet will be retweeted or not is the content-based feature. Furthermore, it was found that the MLP optimized with the swarm intelligence algorithm had better performance compared to the MLP.
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Noble, Frazer, Muqing Xu, and Fakhrul Alam. "Static Hand Gesture Recognition Using Capacitive Sensing and Machine Learning." Sensors 23, no. 7 (2023): 3419. http://dx.doi.org/10.3390/s23073419.
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Automated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a 6×18 array of capacitive sensors that captured five gestures—Palm, Fist, Middle, OK, and Index—of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants’ gestures and tested with one different participant’s gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average F1 score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system.
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Khalifa, Mohamed K. S. "دراسة أداء مُصنِّف مـقـترح لخوارزمية هجينة لإكتشاف التصيد الاحتيالي عبر البريد الإلكتروني". International Science and Technology Journal 36, № 1 (2025): 1–28. https://doi.org/10.62341/mksk1904.
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This research aims to study the effectiveness and performance of the proposed classifier to detect phishing emails, because there is an urgent need to develop information security systems that are accurately and proactively able to recognize phishing messages due to their increasing number and diversity of fraudulent capabilities. Since this type of phishing message manipulates human emotions leading to fears and creates a situation of urgency by claiming that the recipient must take immediate and swift action, which may lead to financial losses or significant data leakage losses. In order to overcome the human weakness in detecting and recognizing phishing emails, this study was conducted because we need to continuously enhance and improve the accuracy and effectiveness of automated and automatic phishing detection algorithms and methods. The proposed classification model utilizes a hybrid algorithm that combines deep learning (DL) multi-layer perceptron (MLP) neural network algorithms and natural language processing (NLP) methods on the body of the received email. This paper highlights the importance of examining the textual features of the body of a mail message for phishing detection, using multi-layer perceptron (MLP) neural networks to analyze the accuracy of detecting phishing through the message text, and because text features represent a relatively new direction of study in the field of email phishing detection. The proposed model was tested on a balanced and labeled dataset of 8579 different messages, and the results showed an improvement in classification accuracy and performance compared to other deep learning methods. The proposed classifier model was evaluated using the following metrics: (Recall, Accuracy, Precision, and F-measure), and the results were obtained - 98.3%, 98.2%, 98.5%, and 98.55%, respectively. The model also showed good performance and took a short time to detect; to produce an overall accuracy rate of over 98.1% and a low false positive rate (FPR) of 0.015. Keywords: Phishing Email detection, MultiLayer Perceptron (MLP) Neural Network, Deep learning (DL), Natural language processing (NLP), Algorithms, Information security
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Diyah, Utami Kusumaning Putri, Nugroho Pratomo Dinar, and Azhari Azhari. "Hybrid convolutional neural networks-support vector machine classifier with dropout for Javanese character recognition." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 21, no. 2 (2023): 346–53. https://doi.org/10.12928/telkomnika.v21i2.24266.
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This research paper explores the hybrid models for Javanese character recognition using 15600 characters gathered from digital and handwritten sources. The hybrid model combines the merit of deep learning using convolutional neural networks (CNN) to involve feature extraction and a machine learning classifier using support vector machine (SVM). The dropout layer also manages overfitting problems and enhances training accuracy. For evaluation purposes, we also compared CNN models with three different architectures with multilayer perceptron (MLP) models with one and two hidden layer(s). In this research, we evaluated three variants of CNN architectures and the hybrid CNN-SVM models on both the accuracy of classification and training time. The experimental outcomes showed that the classification performances of all CNN models outperform the classification performances of both MLP models. The highest testing accuracy for basic CNN is 94.2% when using model 3 CNN. The increment of hidden layers to the MLP model just slightly enhances the accuracy. Furthermore, the hybrid model gained the highest accuracy result of 98.35% for classifying the testing data when combining model 3 CNN with the SVM classifier. We get that the hybrid CNN-SVM model can enhance the accuracy results in the Javanese characters recognition.
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BABY AKULA, R.S.PARMAR, M. P. RAJ, and K. INDUDHAR REDDY. "Prediction for rice yield using data mining approach in Ranga Reddy district of Telangana, India." Journal of Agrometeorology 23, no. 2 (2021): 242–48. http://dx.doi.org/10.54386/jam.v23i2.75.
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 In order to explore the possibility of crop estimation, data mining approach being multidisciplinary was followed. The district of Ranga Reddy, Telangana State, India has been chosen for the study and its year wise average yield data of rice and daily weather over a period of 31 years i.e. from 1988-2019 (30th to 47th Standard Meteorological Weeks). Data mining tool WEKA (V3.8.1). Min- Max Normalization technique followed by Feature Selection algorithm, ‘cfsSubsetEval’ was also adopted to improve quality and accuracy of data mining algorithms. Thus, after cleaning and sorting of data, five classifiers viz., Logistic, MLP (Multi Layer Perceptron), J48 Classifier, LMT (Logistic Model Trees) and PART Classifier were employed over the trained data. The results indicated that the function based and tree based models have better performance over rule based model. In case of function based two models examined, viz., Logistic and MLP, the later performed better over Logistic model. Between tree based two models, LMT performed better over J48. Thus, MLP classifier model found to be the best fit model in predicting rice yields as it recorded an accuracy of 74.19 %, sensitivity of 0.742 and precision of 0.743 as compared with other models. The MLP has also achieved the highest F1 score of (0.742) and MCC (0.581).
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N, Komal Kumar, R. Lakshmi Tulasi, and Vigneswari D. "An ensemble multi-model technique for predicting chronic kidney disease." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1321. http://dx.doi.org/10.11591/ijece.v9i2.pp1321-1326.
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<span lang="EN-US">Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learning for predicting Chronic Kidney Disease (CKD). The proposed hybrid classifier is compared with the machine learning classifiers such as Support Vector Machine (SVM), Decision Tree (DT), C4.5, Particle Swarm Optimized Multi Layer Perceptron (PSO-MLP). The proposed classifier accurately predicts the occurrences of kidney disease by analysis various medical factors. The work comprises of two stages, the first stage consists of obtaining weak decision tree classifiers from C4.5 and in the second stage, the weak classifiers are added to the weighted sum to represent the final output for improved performance of the classifier.</span>
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Balakrishna, Tilakachuri, Jagadeeswara Rao Annam, and Dasari Haritha. "Comparative analysis on liver benchmark datasets and prediction using supervised learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 1043. http://dx.doi.org/10.11591/ijeecs.v36.i2.pp1043-1051.
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Disease diagnosis is most challenging task today. Different datasets are available in web source that contains important features to diagnose the diseases. This paper explores different classification algorithms on medical liver bench mark datasets like BUPA and Indian Liver patient dataset (ILPD). The ILPD is best fit for the model and also gives high classifier accuracy. In proposed model the following classifiers like Naïve Bayes (NB), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) classification, multi-layer perceptron (MLP), artificial neural network (ANN), deep belief network (DBN) and probabilistic neural network (PNN) are used. The results shown that ILPD is best dataset for all classifiers and RF classification in particular is best classifier.
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Tilakachuri, Balakrishna Jagadeeswara Rao Annam Dasari Haritha. "Comparative analysis on liver benchmark datasets and prediction using supervised learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 1043–51. https://doi.org/10.11591/ijeecs.v36.i2.pp1043-1051.
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Disease diagnosis is most challenging task today. Different datasets are available in web source that contains important features to diagnose the diseases. This paper explores different classification algorithms on medical liver bench mark datasets like BUPA and Indian Liver patient dataset (ILPD). The ILPD is best fit for the model and also gives high classifier accuracy. In proposed model the following classifiers like Naïve Bayes (NB), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) classification, multi-layer perceptron (MLP), artificial neural network (ANN), deep belief network (DBN) and probabilistic neural network (PNN) are used. The results shown that ILPD is best dataset for all classifiers and RF classification in particular is best classifier.
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Patel, Shubha V., and S. L. Sunitha. "Analysis of Muscular Paralysis using EMG Signal with Wavelet Decomposition Approach." Asian Journal of Computer Science and Technology 11, no. 1 (2022): 5–16. http://dx.doi.org/10.51983/ajcst-2022.11.1.3241.
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Paralysis refers to temporary or permanent loss of voluntary muscle movement in a body part or region. The degree of muscle function loss determines the severity of paralysis. The muscle function is represented by electrical activity of the muscles. Electromyography is a technique concerned with the analysis of myoelectric signals. EMG allows the determination of muscular activity. EMG signal analysis is performed using the features extracted in time domain, frequency domain and time frequency domain. In this work, the EMG of Amyotrophic Lateral Sclerosis (ALS), Myopathy, and Normal conditions are considered, and the time frequency analysis has been carried out to extract the features using wavelet decomposition approach. The classification of normal and paralyzed condition is carried by four classifier models. The classifier models used are Multi-layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and Nearest Neighbor (NN) models. The standard data set has been used for the purpose. The classification accuracy obtained for MLP is 80%, for RF is 75%, for GB is 79%, and for NN is 69%. MLP show better classification performance over RF, GB, and NN Classifiers.
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Amuthadevi, C., K. Meena, and K. Arthi. "Acute Stage of Brain Stroke Diagnosis Using Hybrid Genetic Algorithm for Optimization of Feature Selection and Classifier." International Journal of Engineering & Technology 7, no. 2.4 (2018): 70. http://dx.doi.org/10.14419/ijet.v7i2.4.11168.
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Brain Stroke is the third leading reason of death or major disabilities and needs computer guided assistance to diagnose at an earliest stage of disease. Stroke results in great physical functioning restrictions, which negatively impacts the quality of life for survivors and also care givers. MRI of brain is mainly used for accurate diagnosis even though its cost is high. In this work, a Hybrid Genetic Algorithm (HGA) is proposed for feature selection with Independent Component analysis and parameters optimization of Multi layer Perceptron (MLP). The classification results are compared with simple KNN and MLP Classifiers.
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Saini, Manish Kumar, and Rajiv Kapoor. "Power Quality Events Classification Using MWT and MLP." Advanced Materials Research 403-408 (November 2011): 4266–71. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4266.
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The work presented uses multiwavelet because of its inherent property to resolve the signal better than all single wavelets. Multiwavelets are based on more than one scaling function. The proposed methodology utilizes an enhanced resolving capability of multiwavelet to recognize power system disturbances. The disturbance classification schema is performed with multiwavelet neural network (MWNN). It performs a feature extraction and a classification algorithm composed of a multiwavelet feature extractor based on norm entropy and a classifier based on a multi-layer perceptron. The performance of this classifier is evaluated by using total 1000 PQ disturbance signals which are generated the based model. The classification performance of different PQ disturbance using proposed algorithm is tested. The rate of average correct classification is about 99.65% for the different PQ disturbance signals and noisy disturbances.
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Obaidullah, S. K., K. C. Santosh, Chayan Halder, Nibaran Das, and Kaushik Roy. "Word-Level Multi-Script Indic Document Image Dataset and Baseline Results on Script Identification." International Journal of Computer Vision and Image Processing 7, no. 2 (2017): 81–94. http://dx.doi.org/10.4018/ijcvip.2017040106.
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Document analysis research starves from the availability of public datasets. Without publicly available dataset, one cannot make fair comparison with the state-of-the-art methods. To bridge this gap, in this paper, the authors propose a word-level document image dataset of 13 different Indic languages from 11 official scripts. It is composed of 39K words that are equally distributed i.e., 3K words per language. For a baseline results, five different classifiers: multilayer perceptron (MLP), fuzzy unordered rule induction algorithm (FURIA), simple logistic (SL), library for linear classifier (LibLINEAR) and bayesian network (BayesNet) classifiers are used with three state-of-the-art features: spatial energy (SE), wavelet energy (WE) and the Radon transform (RT), including their possible combinations. The authors observed that MLP provides better results when all features are used, and achieved the bi-script accuracy of 99.24% (keeping Roman common), 98.38% (keeping Devanagari common) and tri-script accuracy of 98.19% (keeping both Devanagari and Roman common).
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Mainas, Francesca, Bruno Golosio, Alessandra Retico, and Piernicola Oliva. "Exploring Autism Spectrum Disorder: A Comparative Study of Traditional Classifiers and Deep Learning Classifiers to Analyze Functional Connectivity Measures from a Multicenter Dataset." Applied Sciences 14, no. 17 (2024): 7632. http://dx.doi.org/10.3390/app14177632.
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The investigation of functional magnetic resonance imaging (fMRI) data with traditional machine learning (ML) and deep learning (DL) classifiers has been widely used to study autism spectrum disorders (ASDs). This condition is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for the development of effective treatments. This study compares traditional ML and DL classifiers in the analysis of tabular data, in particular, functional connectivity measures obtained from the time series of a public multicenter dataset, and evaluates whether the features that contribute most to the classification task vary depending on the classifier used. Specifically, Support Vector Machine (SVM) classifiers, with both linear and radial basis function (RBF) kernels, and Extreme Gradient Boosting (XGBoost) classifiers are compared against the TabNet classifier (a DL architecture customized for tabular data analysis) and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. Among the latter, SVMs outperform the other classifiers with an AUC of around 75%, whereas the best performances of TabNet and MLP reach 65% and 71% at most, respectively. Furthermore, the analysis of the feature importance showed that the brain regions that contribute the most to the classification task are those primarily responsible for sensory and spatial perception, as well as attention modulation, which is known to be altered in ASDs.
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Chan, Choon Kit, and Girma T. Chala. "OVERFIT PREVENTION IN HUMAN MOTION DATA BY ARTIFICIAL NEURAL NETWORK." Platform : A Journal of Engineering 5, no. 2 (2021): 29. http://dx.doi.org/10.61762/pajevol5iss2art12982.
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Motion analysis has been an active research area for the past decade. Several approaches had been proposed to detect and recognize motion activity for different applications such as motion estimation, modeling, and reconstruction. However, a suitable classifier is required to be embedded with the surveillance system to ensure accurate motion recognition. During these processes, the recognition system compares the captured motion with the motion database in order to recognize the motion activity. However, the classifier can only recognize the motion activities that are closely fit with the database, and overfitting has been an issue in this process. Hence, this paper is aimed at resolving overfitting problem by using Artificial Neural Network (ANN) for motion classification. The motion data was transformed into numerical data with an aid of Kinovea. Data mining software called WEKA was used to perform motion classification. Multi-Layer Perceptron (MLP), which is known as ANN, was modified to recognize different motion activities in the classification process. It was observed that MLP is able to yield classification accuracy of 97.62%. Overfitting issues were also solved by manipulating learning rates in the ANN classifier. A reduced learning rate from 0.3 to 0.1 improved the classification accuracy of jumping motion by up to 12.04%.
 Keywords: overfitting, data pre-processing, classification, multi-layer perceptron, recognition, WEKA
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Hussein, Nashwan Jasim, Alanssari A.N., and Ammar Wisam Altaher. "Classification Cervix Image Using Machine Learning Algorithm to Detect Malignant Area." Webology 19, no. 1 (2022): 3475–80. http://dx.doi.org/10.14704/web/v19i1/web19229.
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Cervical Cancer (CC), sexually transmitted diseases, and cervicovaginal microbiota. In this Sees and Surveys, we center on a few themes in connection to the uterine cervix and barrenness: early cervical cancer and richness saving surgery, cesarean scar deformity, cervical inadequacy, and cervical Mullerian peculiarities. the case of cervix woman cancer proposed in this work revelation and classification system using the modern convolutional updated neural frameworks (CNNs). The cell pictures are fed into a CNNs appear to remove deep- classic algorithm learned highlights. At that point, an extraordinary learning machine (ELM)-based classifier classifies the input pictures. CNNs appear is utilized through trade learning updated algorithm and fine calculate method tuning. Choices to the ELM, multi-layer and perceptron algorithm (MLP) and auto en-algorithm-coder (AE)-based classifiers are in addition work with investigated.
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Rousset, Sylvie, Aymeric Angelo, Toufik Hamadouche, and Philippe Lacomme. "Weight Status Prediction Using a Neuron Network Based on Individual and Behavioral Data." Healthcare 11, no. 8 (2023): 1101. http://dx.doi.org/10.3390/healthcare11081101.
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Background: The worldwide epidemic of weight gain and obesity is increasing in response to the evolution of lifestyles. Our aim is to provide a new predictive method for current and future weight status estimation based on individual and behavioral characteristics. Methods: The data of 273 normal (NW), overweight (OW) and obese (OB) subjects were assigned either to the training or to the test sample. The multi-layer perceptron classifier (MLP) classified the data into one of the three weight statuses (NW, OW, OB), and the classification model accuracy was determined using the test dataset and the confusion matrix. Results: On the basis of age, height, light-intensity physical activity and the daily number of vegetable portions consumed, the multi-layer perceptron classifier achieved 75.8% accuracy with 90.3% for NW, 34.2% for OW and 66.7% for OB. The NW and OW subjects showed the highest and the lowest number of true positives, respectively. The OW subjects were very often confused with NW. The OB subjects were confused with OW or NW 16.6% of the time. Conclusions: To increase the accuracy of the classification, a greater number of data and/or variables are needed.
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Aker, Elhadi, Mohammad Lutfi Othman, Veerapandiyan Veerasamy, Ishak bin Aris, Noor Izzri Abdul Wahab, and Hashim Hizam. "Fault Detection and Classification of Shunt Compensated Transmission Line Using Discrete Wavelet Transform and Naive Bayes Classifier." Energies 13, no. 1 (2020): 243. http://dx.doi.org/10.3390/en13010243.
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This paper presents the methodology to detect and identify the type of fault that occurs in the shunt compensated static synchronous compensator (STATCOM) transmission line using a combination of Discrete Wavelet Transform (DWT) and Naive Bayes (NB) classifiers. To study this, the network model is designed using Matlab/Simulink. Different types of faults, such as Line to Ground (LG), Line to Line (LL), Double Line to Ground (LLG) and the three-phase (LLLG) fault, are applied at disparate zones of the system, with and without STATCOM, considering the effect of varying fault resistance. The three-phase fault current waveforms obtained are decomposed into several levels using Daubechies (db) mother wavelet of db4 to extract the features, such as the standard deviation (SD) and energy values. Then, the extracted features are used to train the classifiers, such as Multi-Layer Perceptron Neural Network (MLP), Bayes and the Naive Bayes (NB) classifier to classify the type of fault that occurs in the system. The results obtained reveal that the proposed NB classifier outperforms in terms of accuracy rate, misclassification rate, kappa statistics, mean absolute error (MAE), root mean square error (RMSE), percentage relative absolute error (% RAE) and percentage root relative square error (% RRSE) than both MLP and the Bayes classifier.
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Mohmad Hassim, Yana Mazwin, and Rozaida Ghazali. "Using Artificial Bee Colony to Improve Functional Link Neural Network Training." Applied Mechanics and Materials 263-266 (December 2012): 2102–8. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2102.
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Artificial Neural Networks have emerged as an important tool for classification and have been widely used to classify non-linearly separable pattern. The most popular artificial neural networks model is a Multilayer Perceptron (MLP) that is able to perform classification task with significant success. However due to the complexity of MLP structure and also problems such as local minima trapping, over fitting and weight interference have made neural network training difficult. Thus, the easy way to avoid these problems is by removing the hidden layers. This paper presents the ability of Functional Link Neural Network (FLNN) in overcoming the complexity structure of MLP, using it single layer architecture and proposes an Artificial Bee Colony (ABC) optimization for training the FLNN. The proposed technique is expected to provide better learning scheme for a classifier in order to get more accurate classification result.
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Yaseen, Muhammad Waseem, Muhammad Awais, Khuram Riaz, Muhammad Babar Rasheed, Muhammad Waqar, and Sajid Rasheed. "Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan." Archives of Hydro-Engineering and Environmental Mechanics 69, no. 1 (2022): 59–77. http://dx.doi.org/10.2478/heem-2022-0005.
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Abstract Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.
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Sultana, Jabeen, Abdul Khader Jilani, and . "Predicting Breast Cancer Using Logistic Regression and Multi-Class Classifiers." International Journal of Engineering & Technology 7, no. 4.20 (2018): 22. http://dx.doi.org/10.14419/ijet.v7i4.20.22115.
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The primary identification and prediction of type of the cancer ought to develop a compulsion in cancer study, in order to assist and supervise the patients. The significance of classifying cancer patients into high or low risk clusters needs commanded many investigation teams, from the biomedical and the bioinformatics area, to learn and analyze the application of machine learning (ML) approaches. Logistic Regression method and Multi-classifiers has been proposed to predict the breast cancer. To produce deep predictions in a new environment on the breast cancer data. This paper explores the different data mining approaches using Classification which can be applied on Breast Cancer data to build deep predictions. Besides this, this study predicts the best Model yielding high performance by evaluating dataset on various classifiers. In this paper Breast cancer dataset is collected from the UCI machine learning repository has 569 instances with 31 attributes. Data set is pre-processed first and fed to various classifiers like Simple Logistic-regression method, IBK, K-star, Multi-Layer Perceptron (MLP), Random Forest, Decision table, Decision Trees (DT), PART, Multi-Class Classifiers and REP Tree. 10-fold cross validation is applied, training is performed so that new Models are developed and tested. The results obtained are evaluated on various parameters like Accuracy, RMSE Error, Sensitivity, Specificity, F-Measure, ROC Curve Area and Kappa statistic and time taken to build the model. Result analysis reveals that among all the classifiers Simple Logistic Regression yields the deep predictions and obtains the best model yielding high and accurate results followed by other methods IBK: Nearest Neighbor Classifier, K-Star: instance-based Classifier, MLP- Neural network. Other Methods obtained less accuracy in comparison with Logistic regression method.
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Islam, Tanvirul, Ashik Iqbal Prince, Md Mehedee Zaman Khan, Md Ismail Jabiullah, and Md Tarek Habib. "An in-depth exploration of Bangla blog post classification." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 742–49. http://dx.doi.org/10.11591/eei.v10i2.2873.
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Bangla blog is increasing rapidly in the era of information, and consequently, the blog has a diverse layout and categorization. In such an aptitude, automated blog post classification is a comparatively more efficient solution in order to organize Bangla blog posts in a standard way so that users can easily find their required articles of interest. In this research, nine supervised learning models which are Support Vector Machine (SVM), multinomial naïve Bayes (MNB), multi-layer perceptron (MLP), k-nearest neighbours (k-NN), stochastic gradient descent (SGD), decision tree, perceptron, ridge classifier and random forest are utilized and compared for classification of Bangla blog post. Moreover, the performance on predicting blog posts against eight categories, three feature extraction techniques are applied, namely unigram TF-IDF (term frequency-inverse document frequency), bigram TF-IDF, and trigram TF-IDF. The majority of the classifiers show above 80% accuracy. Other performance evaluation metrics also show good results while comparing the selected classifiers.
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Tanvirul, Islam, Iqbal Prince Ashik, Mehedee Zaman Khan Md., Ismail Jabiullah Md., and Tarek Habib Md. "An in-depth exploration of Bangla blog post classification." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 742~749. https://doi.org/10.11591/eei.v10i2.2873.
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Bangla blog is increasing rapidly in the era of information, and consequently, the blog has a diverse layout and categorization. In such an aptitude, automated blog post classification is a comparatively more efficient solution in order to organize Bangla blog posts in a standard way so that users can easily find their required articles of interest. In this research, nine supervised learning models which are support vector machine (SVM), multinomial naïve Bayes (MNB), multi-layer perceptron (MLP), k-nearest neighbours (k-NN), stochastic gradient descent (SGD), decision tree, perceptron, ridge classifier and random forest are utilized and compared for classification of Bangla blog post. Moreover, the performance on predicting blog posts against eight categories, three feature extraction techniques are applied, namely unigram TF-IDF (term frequency-inverse document frequency), bigram TF-IDF, and trigram TF-IDF. The majority of the classifiers show above 80% accuracy. Other performance evaluation metrics also show good results while comparing the selected classifiers.
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Chandran, Bhuvaneswari. "An Image based Diagnostic System for Lung Disease Classification." Journal of Communications Technology, Electronics and Computer Science 3 (December 29, 2015): 6. http://dx.doi.org/10.22385/jctecs.v3i0.6.
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Model-based detection and classification of nodules are two major steps in CAD systems design and evaluation. A common health problem, lung diseases are the most prevailing medical conditions throughout the world. In this paper, Lung diseases are automatically classified as Emphysema, Bronchitis, Pleural effusion and normal lung.The lung CT images are taken as input, preprocessing is applied, feature extraction is done by various methods such as Gabor filter extracts the texture features, walsh hadamard transform extracts the pixel co-efficient values, and a fusion method is proposed in this work which extracts the median absolute deviation values. Feature selection including statistical correlation based methods and Genetic Algorithm for searching in feature vector space are investigated. Four types of the classifiers are used where the Multi-Layer Perceptron Neural Network (MLP-NN) classifier with proposed fusion feature extraction method, genetic algorithm feature selection method gives promising result of 91% accuracy than J48, K- Nearest Neighbour and Naïve bayes classifiers.
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IŞIK, Gültekin. "Improving Plant Disease Recognition Through Gradient-Based Few-shot Learning with Attention Mechanisms." Journal of the Institute of Science and Technology 13, no. 3 (2023): 1482–95. http://dx.doi.org/10.21597/jist.1283491.
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This study investigates the use of few-shot learning algorithms to improve classification performance in situations where traditional deep learning methods fail due to a lack of training data. Specifically, we propose a few-shot learning approach using the Almost No Inner Loop (ANIL) algorithm and attention modules to classify tomato diseases in the Plant Village dataset. The attended features obtained from the five separate attention modules are classified using a Multi Layer Perceptron (MLP) classifier, and the soft voting method is used to weigh the classification scores from each classifier. The results demonstrate that our proposed approach achieves state-of-the-art accuracy rates of 97.05% and 97.66% for 10-shot and 20-shot classification, respectively. Our approach demonstrates the potential for incorporating attention mechanisms in feature extraction processes and suggests new avenues for research in few-shot learning methods.
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Hakeel, Mohamed, Aji Primajaya, and E. Haodudin Nurfikli. "SYMPTOM-BASED DISEASE PREDICTION USING MACHINE LEARNING." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 4 (2025): 6704–8. https://doi.org/10.36040/jati.v9i4.14087.
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There are now more opportunities to increase diagnostic accessibility and accuracy thanks to the application of machine learning (ML) in healthcare, especially in environments with limited resources. The Random Forest Classifier (RFC) and Multi-Layer Perceptron (MLP) models emphasize this study's strong framework for symptom-based disease prediction utilizing machine learning methods. Our approach emphasizes the significance of data preparation, feature engineering, and model evaluation while addressing important issues, including missing data, symptom overlap, and ethical implications using Kaggle datasets. According to our findings, the RFC model performs better than the MLP classifier, with 99% accuracy. We also created an interactive platform for disease prediction, data addition, and model retraining using a web application built using Streamlit. Especially in poverty-stricken areas, this approach provides a scalable and dependable tool for early disease diagnosis, lowering diagnostic mistakes and enhancing healthcare accessibility.
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Steven Joses, Donata Yulvida, and Siti Rochimah. "Pendekatan Metode Ensemble Learning untuk Prakiraan Cuaca menggunakan Soft Voting Classifier." Journal of Applied Computer Science and Technology 5, no. 1 (2024): 72–80. http://dx.doi.org/10.52158/jacost.v5i1.741.
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Weather conditions are one of the crucial factors that need attention. Changes in weather conditions significantly impact various activities. Weather condition changes are determined by numerous factors, often occurring within a relatively short period in the atmosphere, such as pressure, wind speed, rainfall, temperature, and other atmospheric phenomena. Issues in weather forecasting arise due to several factors, namely the fluctuating atmospheric conditions. This research proposes the development of a weather forecasting model using the ensemble learning method approach. The weather data used consist of 33746 records with attributes used after preprocessing, namely Temperature, Dew Point, Humidity, Wind Speed, Wind Gust, Pressure, Precipitation, and Condition. Testing in this research employs several single-machine learning methods such as K-Nearest Neighbor (KNN), Logistic Regression, Random Forest, Naive Bayes, and Multi-Layer Perceptron. The Naive Bayes method using default parameters achieves a high accuracy of 99.00%. In the ensemble method, combinations of three methods exhibit excellent accuracy for all combinations. The best combination methods are found in the Soft Voting Classifier method (Random Forest, MLP, Naive Bayes), Soft Voting Classifier (Logistic Regression, MLP, Naive Bayes), and Soft Voting Classifier (Random Forest, KNN, Naive Bayes) with an accuracy of 99.03%.
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Yang, Yingjian, Wei Li, Yingwei Guo, et al. "Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier." Mathematical Biosciences and Engineering 19, no. 8 (2022): 7826–55. http://dx.doi.org/10.3934/mbe.2022366.
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<abstract> <p>Computed tomography (CT) has been the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Radiomics features extracted from the region of interest in chest CT images have been widely used for lung diseases, but they have not yet been extensively investigated for COPD. Therefore, it is necessary to understand COPD from the lung radiomics features and apply them for COPD diagnostic applications, such as COPD stage classification. Lung radiomics features are used for characterizing and classifying the COPD stage in this paper. First, 19 lung radiomics features are selected from 1316 lung radiomics features per subject by using Lasso. Second, the best performance classifier (multi-layer perceptron classifier, MLP classifier) is determined. Third, two lung radiomics combination features, Radiomics-FIRST and Radiomics-ALL, are constructed based on 19 selected lung radiomics features by using the proposed lung radiomics combination strategy for characterizing the COPD stage. Lastly, the 19 selected lung radiomics features with Radiomics-FIRST/Radiomics-ALL are used to classify the COPD stage based on the best performance classifier. The results show that the classification ability of lung radiomics features based on machine learning (ML) methods is better than that of the chest high-resolution CT (HRCT) images based on classic convolutional neural networks (CNNs). In addition, the classifier performance of the 19 lung radiomics features selected by Lasso is better than that of the 1316 lung radiomics features. The accuracy, precision, recall, F1-score and AUC of the MLP classifier with the 19 selected lung radiomics features and Radiomics-ALL were 0.83, 0.83, 0.83, 0.82 and 0.95, respectively. It is concluded that, for the chest HRCT images, compared to the classic CNN, the ML methods based on lung radiomics features are more suitable and interpretable for COPD classification. In addition, the proposed lung radiomics combination strategy for characterizing the COPD stage effectively improves the classifier performance by 12% overall (accuracy: 3%, precision: 3%, recall: 3%, F1-score: 2% and AUC: 1%).</p> </abstract>
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Hou, Jiani, and Aimin Zhu. "Identification of Usefulness for Online Reviews Based on Grounded Theory and Multilayer Perceptron Neural Network." Applied Sciences 13, no. 9 (2023): 5321. http://dx.doi.org/10.3390/app13095321.
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In the context of the continuous development of e-commerce platforms and consumer shopping patterns, online reviews of goods are increasing. At the same time, its commercial value is self-evident, and many merchants and consumers manipulate online reviews for profit purposes. Therefore, a method based on Grounded theory and Multi-Layer Perceptron (MLP) neural network is proposed to identify the usefulness of online reviews. Firstly, the Grounded theory is used to collect and analyze the product purchasing experiences of 35 consumers, and the characteristics of the usefulness of online reviews in each stage of purchase decision-making are extracted. Secondly, the MLP neural network classifier is used to identify the usefulness of online reviews. Finally, relevant comments are captured as the subject and compared with the traditional classifier algorithm to verify the effectiveness of the proposed method. The experimental results show that the feature extraction method considering consumers’ purchase decisions can improve the classification effect to a certain extent and provide some guidance and suggestions for enterprises in the practice of operating online stores.
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Elsadig, Muawia A., Abdelrahman Altigani, Yasir Mohamed, et al. "Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks." World Electric Vehicle Journal 16, no. 6 (2025): 324. https://doi.org/10.3390/wevj16060324.
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Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a number of security models that have recently been introduced to counter VANET security attacks with a focus on machine learning detection methods. This confirms that several challenges remain unsolved. Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed by applying a random oversampling technique. The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers, including AdaBoost, decision tree (DT), K-nearest neighbors (KNNs), and multi-layer perceptron (MLP). To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. It causes negligible classification errors.
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Sharma, Sandhya, Sheifali Gupta, and Neeraj Kumar. "A Comprehensive Study on the Recognition of Gurmukhi Script." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2674–77. http://dx.doi.org/10.1166/jctn.2020.8965.
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Nowadays, we process all the important information of our lives electronically. Due to the involvement of computers in every sphere there may be a need to develop some efficient and fast techniques so that records can be easily transferred between people and computer systems. Offline text recognition provides an interface between humans and computers. Many researchers are working to recognize the text of Indian scripts like Bangla, Devanagari, Gurmukhi etc. but it is still a challenge to exchange data between people and computers due to the different writing style of the people and very little work has been done for Gurmukhi. In this article different accuracy results are reviewed which are achieved by different researchers using different classification techniques. Various classifiers for the recognition of characters like Support Vector Machine (SVM) based classifier (Upper zone classifier and Lower zone classifier), Hidden Markov Model (HMM) by using a set of features of the normalized x–y traces of the stroke, DCT2 feature set using Linear SVM classifier, Polynomial SVM with iDCT2 features, Multi layered perceptron (MLP) neural network and Knearest neighbor (KNN) etc. classifiers have been used.
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Selwal, Arvind, and Ifrah Raoof. "A multi-layer perceptron based improved thyroid disease prediction system." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 1 (2020): 524. http://dx.doi.org/10.11591/ijeecs.v17.i1.pp524-532.
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<p>A challenging task for the medical science is to achieve the accurate diagnosis of diseases prior to its treatment. A pattern classifier is used for solving complex and non-separable computing problems in different fields like biochemical analysis, image processing and chemical analysis etc .The accuracy for thyroid diagnosis system may be improved by considering few additional attributes like heredity ,age, anti-bodies etc. In this paper, a thyroid disease prediction system is developed using multilayer perceptron (MLP). The proposed system uses 7–11 attributes of individuals to classify them in normal, hyperthyroid and hypothyroid classes. The proposed model uses gradient descent backpropogation algorithm for training the multilayer perceptron using dataset of 120 subjects. The thyroid prediction system promises excellent overall accuracy of ~100% for 11 attributes. However, the system results in a lower accuracy of 66.7% using 11 attributes and 70% using 7 attributes with 30 subjects.</p>
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Bhattacharya, Saheli, Laura Bennet, Joanne O. Davidson, and Charles P. Unsworth. "Multi-layer perceptron classification & quantification of neuronal survival in hypoxic-ischemic brain image slices using a novel gradient direction, grey level co-occurrence matrix image training." PLOS ONE 17, no. 12 (2022): e0278874. http://dx.doi.org/10.1371/journal.pone.0278874.
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Hypoxic ischemic encephalopathy (HIE) is a major global cause of neonatal death and lifelong disability. Large animal translational studies of hypoxic ischemic brain injury, such as those conducted in fetal sheep, have and continue to play a key role in furthering our understanding of the cellular and molecular mechanisms of injury and developing new treatment strategies for clinical translation. At present, the quantification of neurons in histological images consists of slow, manually intensive morphological assessment, requiring many repeats by an expert, which can prove to be time-consuming and prone to human error. Hence, there is an urgent need to automate the neuron classification and quantification process. In this article, we present a ’Gradient Direction, Grey level Co-occurrence Matrix’ (GD-GLCM) image training method which outperforms and simplifies the standard training methodology using texture analysis to cell-classification. This is achieved by determining the Grey level Co-occurrence Matrix of the gradient direction of a cell image followed by direct passing to a classifier in the form of a Multilayer Perceptron (MLP). Hence, avoiding all texture feature computation steps. The proposed MLP is trained on both healthy and dying neurons that are manually identified by an expert and validated on unseen hypoxic-ischemic brain slice images from the fetal sheep in utero model. We compared the performance of our classifier using the gradient magnitude dataset as well as the gradient direction dataset. We also compare the performance of a perceptron, a 1-layer MLP, and a 2-layer MLP to each other. We demonstrate here a way of accurately identifying both healthy and dying cortical neurons obtained from brain slice images of the fetal sheep model under global hypoxia to high precision by identifying the most minimised MLP architecture, minimised input space (GLCM size) and minimised training data (GLCM representations) to achieve the highest performance over the standard methodology.