scholarly journals Prediksi Produktivitas Tanaman Padi di Kabupaten Karawang Menggunakan Bayesian Networks

2017 ◽  
Vol 9 (4) ◽  
Author(s):  
Betha Nurina Sari ◽  
Hendi Permana ◽  
Kardo Trihandoko ◽  
Asep Jamaludin ◽  
Yuyun Umaidah
Keyword(s):  
Bayesian Networks ◽  
Learning Process ◽  
Structure Learning ◽  
Confusion Matrix ◽  
Numerical Data ◽  
Training Data ◽  
Nominal Data ◽  
Productivity Level ◽  
Rice Productivity ◽  
Data Discretization

This research is aimed to build a model for predicting rice productivity level in Karawang district. The prediction using Bayesian Networks allowed three stages, pre-processing of data, implementation and evaluation stages. Pre-processing is transformation of numerical data into nominal data by using two scenarios, using threshold mean and discretization. Implementation stage is to apply Bayesian Networks algorithm, that is through structure learning process and parameter learning. The learning process of structures and parameters on bayesian networks using CaMML 1.41 software. Evaluation of Bayesian Networks performance in predicting rice productivity with confusion matrix, ie calculating prediction accuracy and log loss. The experiment results show the satisfactory results, the accuracy above 90%. The best model generated from pre-processing using the data discretization and 5-year training and 1-year testing data. This explain that the selection techniques of pre-processing and the technique of dividing the training data and testing the data affect the results of the performance evaluation of the structure of Bayesian Networks.

scholarly journals Falcon Optimization Algorithm for Bayesian Networks Structure Learning

2021 ◽  
Vol 22 (4) ◽  
Author(s):  
Shahab Wahhab Kareem ◽  
Mehmet Cudi Okur
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Optimization Algorithm ◽  
Structure Learning ◽  
Confusion Matrix ◽  
Optimal Solution ◽  
Score Function ◽  
Scientific Models ◽  
Data Sets ◽  
Networks Structure

In machine-learning, one of the useful scientific models for producing the structure of knowledge is Bayesian network, which can draw probabilistic dependency relationships between variables. The score and search is a method used for learning the structure of a Bayesian network. The authors apply the Falcon Optimization Algorithm (FOA) as a new approach to learning the structure of Bayesian networks. This paper uses the Reversing, Deleting, Moving and Inserting operations to adopt the FOA for approaching the optimal solution of Bayesian network structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is compared with Pigeon Inspired optimization, Greedy Search, and Simulated Annealing using the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques utilizing several benchmark data sets. As shown by the evaluations, the proposed method has more reliable performance than the other algorithms including producing better scores and accuracy values.

Structure Learning of Bayesian Networks Using Elephant Swarm Water Search Algorithm

2020 ◽  
Vol 11 (2) ◽  
pp. 19-30
Author(s):  
Shahab Wahhab Kareem ◽  
Mehmet Cudi Okur
Keyword(s):  
Bayesian Networks ◽  
Structure Learning ◽  
Search Algorithm ◽  
Confusion Matrix ◽  
Score Function ◽  
Analytical Models ◽  
Data Sets ◽  
Novel Approach ◽  
Networks Structure ◽  
Structure Of Knowledge

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Bayesian networks can represent probabilistic dependency relationships among the variables. One strategy of Bayesian Networks structure learning is the score and search technique. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) as a novel approach to Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with simulated annealing and greedy search using BDe score function. The authors have also investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of the evaluations, the proposed algorithm has better performance than the other algorithms and produces better scores and accuracy values.

scholarly journals Multimodal Sensor Data Fusion for Activity Recognition Using Filtered Classifier

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1262 ◽  
Author(s):  
Muhammad Razzaq ◽  
Ian Cleland ◽  
Chris Nugent ◽  
Sungyoung Lee
Keyword(s):  
Activity Recognition ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Sensor Data ◽  
Test Dataset ◽  
Sensory Data ◽  
Multimodal Sensors ◽  
Imbalanced Class Distribution ◽  
Single Modality

Activity recognition (AR) is a subtask in pervasive computing and context-aware systems, which presents the physical state of human in real-time. These systems offer a new dimension to the widely spread applications by fusing recognized activities obtained from the raw sensory data generated by the obtrusive as well as unobtrusive revolutionary digital technologies. In recent years, an exponential growth has been observed for AR technologies and much literature exists focusing on applying machine learning algorithms on obtrusive single modality sensor devices. However, University of Jaén Ambient Intelligence (UJAmI), a Smart Lab in Spain has initiated a 1st UCAmI Cup challenge by sharing aforementioned varieties of the sensory data in order to recognize the human activities in the smart environment. This paper presents the fusion, both at the feature level and decision level for multimodal sensors by preprocessing and predicting the activities within the context of training and test datasets. Though it achieves 94% accuracy for training data and 47% accuracy for test data. However, this study further evaluates post-confusion matrix also and draws a conclusion for various discrepancies such as imbalanced class distribution within the training and test dataset. Additionally, this study also highlights challenges associated with the datasets for which, could improve further analysis.

scholarly journals The engineering skills training process modeling using dynamic bayesian nets

2021 ◽  
pp. 87-96
Author(s):  
Andrey Chukhray ◽  
Olena Havrylenko
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Learning Process ◽  
Probabilistic Inference ◽  
Dynamic Bayesian Network ◽  
Computer Training ◽  
Skills Training ◽  
Dynamic Bayesian Networks ◽  
Competence Level ◽  
Intelligent Computer

The subject of research in the article is the process of intelligent computer training in engineering skills. The aim is to model the process of teaching engineering skills in intelligent computer training programs through dynamic Bayesian networks. Objectives: To propose an approach to modeling the process of teaching engineering skills. To assess the student competence level by considering the algorithms development skills in engineering tasks and the algorithms implementation ability. To create a dynamic Bayesian network structure for the learning process. To select values for conditional probability tables. To solve the problems of filtering, forecasting, and retrospective analysis. To simulate the developed dynamic Bayesian network using a special Genie 2.0-environment. The methods used are probability theory and inference methods in Bayesian networks. The following results are obtained: the development of a dynamic Bayesian network for the educational process based on the solution of engineering problems is presented. Mathematical calculations for probabilistic inference problems such as filtering, forecasting, and smoothing are considered. The solution of the filtering problem makes it possible to assess the current level of the student's competence after obtaining the latest probabilities of the development of the algorithm and its numerical calculations of the task. The probability distribution of the learning process model is predicted. The number of additional iterations required to achieve the required competence level was estimated. The retrospective analysis allows getting a smoothed assessment of the competence level, which was obtained after the task's previous instance completion and after the computation of new additional probabilities characterizing the two checkpoints implementation. The solution of the described probabilistic inference problems makes it possible to provide correct information about the learning process for intelligent computer training systems. It helps to get proper feedback and to track the student's competence level. The developed technique of the kernel of probabilistic inference can be used as the decision-making model basis for an automated training process. The scientific novelty lies in the fact that dynamic Bayesian networks are applied to a new class of problems related to the simulation of engineering skills training in the process of performing algorithmic tasks.

scholarly journals Identifikasi Kematangan Cabai Menggunakan Operasi Morfologi (Opening dan Closing) dan Metode Backpropagation

SISTEMASI ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Khairullah Khairullah ◽  
Erwin Dwika Putra
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Confusion Matrix ◽  
Training Data ◽  
Morphological Operations ◽  
Morphological Operation ◽  
Backpropagation Method ◽  
Grouping Method ◽  
Opening And Closing

AbstrakIdentifikasi kualitas buah cabai biasanya masih menggunakan cara visual secara langsung atau sortir secara manual oleh petani, dengan menggunakan sistem ini sering kali terjadi beberapa kesalahan setiap melakukan sortir yang disebabkan oleh petani yang melakukan sortir merasa terlalu lelah. Dengan menggunakan komputasi pengolahan citra digital, untuk melakukan identifikasi pengelompokan buah cabai yang matang dan mentah dapat membantu para petani, Teknik pengelompokan ini akan menggunakan metode pengelompokan berdasarkan warna. Metode pengelompokan tersebut sebelumnya akan dilakukan operasi morfologi pada citra yang telah diambil. Pendekatan operasi morfologi pada penelitian ini adalah Opening and Closing, pada operasi morfologi akan menghilangkan noise dan menebalkan objek dari inputan gambar. Metode Bacpropagatioan akan mengolah data latih sebanyak 10 data latih mendapatkan 6 iterasi perhitungan dan setelah diuji menggunakan data uji hasil yang didapatkan yaitu tingkat pengenalan rata-rat mendapatkan perhitungan sebanyak 7 iterasi metode Bacpropagation. Hasil dari penelitian ini juga dihitung menggunakan Confusion Matrix dimana nilai Precision 90%, Recall 74%, dan Accuracy 70%, maka dapat disimpulkan bahwa Operasi Morfologi dan Metode Backpropagation dapat digunakan untuk mengidentifikasi objek cabai.Kata Kunci: backpropagation, morfologi, identifikasi, opening and closing  AbstractIdentification of the quality of chili fruit is usually still using a visual way directly or sorting manually by farmers, using this system often occurs several errors, every sorting caused by farmers who do the sorting feel too tired. By using digital image processing computing, to identify the grouping of ripe and raw chili fruits can help farmers, this grouping technique will use a method of grouping based on color. The grouping method will previously perform morphological surgery on the image that has been taken. The morphological operation approach in this study is Opening and Closing, in morphological operations will eliminate noise and thicken objects from image input. Bacpropagatioan method will process training data as much as 10 training data get 6 iterations of calculations and after being tested using the test data obtained results that is the level of introduction of the average rat get a calculation of 7 iterations bacpropagation method. The results of this study were also calculated using Confusion Matrix where precision values of 90%, Recall 74%, and Accuracy 70%, it can be concluded that Morphological Operations and Backpropagation Method can be used to identify chili objects.Keywords: backpropagation, morfologi, identification, opening and closing

scholarly journals COMPARISON OF NAIVE BAYES ALGORITHM AND C.45 ALGORITHM IN CLASSIFICATION OF POOR COMMUNITIES RECEIVING NON CASH FOOD ASSISTANCE IN WANASARI VILLAGE KARAWANG REGENCY

2020 ◽  
Vol 17 (1) ◽  
pp. 37-42
Author(s):  
Yuris Alkhalifi ◽  
Ainun Zumarniansyah ◽  
Rian Ardianto ◽  
Nila Hardi ◽  
Annisa Elfina Augustia
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Total Sample ◽  
Naïve Bayes ◽  
Food Assistance ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Non-Cash Food Assistance or Bantuan Pangan Non-Tunai (BPNT) is food assistance from the government given to the Beneficiary Family (KPM) every month through an electronic account mechanism that is used only to buy food at the Electronic Shop Mutual Assistance Joint Business Group Hope Family Program (e-Warong KUBE PKH ) or food traders working with Bank Himbara. In its distribution, BPNT still has problems that occur that are experienced by the village apparatus especially the apparatus of Desa Wanasari on making decisions, which ones are worthy of receiving (poor) and not worthy of receiving (not poor). So one way that helps in making decisions can be done through the concept of data mining. In this study, a comparison of 2 algorithms will be carried out namely Naive Bayes Classifier and Decision Tree C.45. The total sample used is as much as 200 head of household data which will then be divided into 2 parts into validation techniques is 90% training data and 10% test data of the total sample used then the proposed model is made in the RapidMiner application and then evaluated using the Confusion Matrix table to find out the highest level of accuracy from 2 of these methods. The results in this classification indicate that the level of accuracy in the Naive Bayes Classifier method is 98.89% and the accuracy level in the Decision Tree C.45 method is 95.00%. Then the conclusion that in this study the algorithm with the highest level of accuracy is the Naive Bayes Classifier algorithm method with a difference in the accuracy rate of 3.89%.

Evaluation of Bayesian Network Structure Learning Using Elephant Swarm Water Search Algorithm

2020 ◽  
pp. 139-159
Author(s):  
Shahab Wahhab Kareem ◽  
Mehmet Cudi Okur
Keyword(s):  
Simulated Annealing ◽  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Search Algorithm ◽  
Confusion Matrix ◽  
Data Sets ◽  
Greedy Search ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning which can represent probabilistic dependency relationships among the variables. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) for Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with Pigeon Inspired Optimization, Simulated Annealing, Greedy Search, Hybrid Bee with Simulated Annealing, and Hybrid Bee with Greedy Search using BDeu score function as a metric for all algorithms. They investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of evaluations, the proposed algorithm achieves better performance than the other algorithms and produces better scores as well as the better values.

Bayesian Networks for Image Understanding

2007 ◽  
pp. 128-150
Author(s):  
Andreas Savaki ◽  
Jiebo Luo ◽  
Michael Kane
Keyword(s):  
Bayesian Networks ◽  
Object Detection ◽  
Bayesian Network ◽  
Structure Learning ◽  
Image Understanding ◽  
Semantic Features ◽  
Scene Classification ◽  
Low Level ◽  
Object Parts ◽  
Scene Content

Image understanding deals with extracting and interpreting scene content for use in various applications. In this chapter, we illustrate that Bayesian networks are particularly well-suited for image understanding problems, and present case studies in indoor-outdoor scene classification and parts-based object detection. First, improved scene classification is accomplished using both low-level features, such as color and texture, and semantic features, such as the presence of sky and grass. Integration of low-level and semantic features is achieved using a Bayesian network framework. The network structure can be determined by expert opinion or by automated structure learning methods. Second, object detection at multiple views relies on a parts-based approach, where specialized detectors locate object parts and a Bayesian network acts as the arbitrator in order to determine the object presence. In general, Bayesian networks are found to be powerful integrators of different features and help improve the performance of image understanding systems.

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