Fingerlings mass estimation: A comparison between deep and shallow learning algorithms
Adair da Silva OliveiraDiego André Sant’AnaMarcio Carneiro Brito PacheVanir GarciaVanessa Aparecida de Moares WeberGilberto AstolfiFabricio de Lima WeberGeazy Vilharva MenezesGabriel Kirsten MenezesPedro Lucas França AlbuquerqueCelso Soares CostaEduardo Quirino Arguelho de QueirozJoão Victor Araújo RozalesMilena Wolff FerreiraMarco Hiroshi NakaHemerson Pistori
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The paper presents some results regarding the automatic mass estimation of Pintado Real fingerlings, using machine learning techniques to support the fish production process. For this purpose, an image dataset called FISHCV1206FSEG, was created which is composed of 1206 images of fingerlings with their respective annotated masses. Through the fish contours, the area and perimeter were extracted, and submitted to the J48, SVM, and KNN classification algorithms and a linear regression algorithm. The images were also submitted to ResNet50, InceptionV3, Exception, VGG16, and VGG19 convolutional neural networks. As a result, the classification algorithm J48 reached an accuracy of 58.2% and a linear regression model capable of predicting the mass of a Pintado Real fingerling with a mean squared error of 1.5 g. The convolutional neural network ResNet50 obtained an accuracy of 67.08%. We can highlight the contributions of this work through the presentation of a methodology to classify the mass of fingerlings in a non-invasive way and by the analyses and comparing results of different machine learning algorithms for classification and regression.Keywords:
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A decision tree is an important classification technique in data mining classification. Decision trees have proved to be valuable tools for the classification, description, and generalization of data. J48 is a decision tree algorithm which is used to create classification model. J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. In this paper, we present the method of improving accuracy for decision tree mining with data preprocessing. We applied the supervised filter discretization on J48 algorithm to construct a decision tree. We compared the results with the J48 without discretization. The results obtained from experiments show that accuracy of J48 after discretization is better than J48 before discretization.
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Computerization of society has substantially improved the ability to generate and collect data from a variety of sources. A large amount of data has flooded almost every aspect of people's lives. AMIK HASS Bandung has an Informatic Management Study Program consisting of three areas of concentration that can be selected by students in the fourth semester including Computerized Accounting, Computer Administration, and Multimedia. The determination of concentration selection should be precise based on past data, so the academic section must have a pattern or rule to predict concentration selection. In this work, the data mining techniques were using Naive Bayes and Decision Tree J48 using WEKA tools. The data set used in this study was 111 with a split test percentage mode of 75% used as training data as the model formation and 25% as test data to be tested against both models that had been established. The highest accuracy result obtained on Naive Bayes which is obtaining a 71.4% score consisting of 20 instances that were properly clarified from 28 training data. While Decision Tree J48 has a lower accuracy of 64.3% consisting of 18 instances that are properly clarified from 28 training data. In Decision Tree J48 there are 4 patterns or rules formed to determine concentration selection so that the academic section can assist students in determining concentration selection.
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The critical issue to the academic community of higher education is to monitor the progress of students’ academic performance. We can use data mining techniques for this purpose. J48 algorithm is one of the famous classification algorithms present today to generate decision trees in data mining technique. The data set used in this study is taken from University of Computer Studies (Mandalay). Weka machine learning tool is applied to make classification. In this work, we tested result classification accuracy was computed. This J48 classification algorithm give accuracy with 78.2%.
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This short paper compares the performance of three popular decision tree algorithms: C4.5, C5.0, and WEKA’s J48. These decision tree algorithms are all related in that C5.0 is an updated commercial version of C4.5 and J48 is an implementation of the C4.5 algorithm under the WEKA data mining platform. The purpose of this paper is to verify the explicit or implied performance claims for these algorithms—namely that C5.0 is superior to C4.5 and that J48 mimics the performance of C4.5. Our results are quite surprising and contradict these claims. This is significant because existing work that is based on these claims (e.g., J48 being equivalent to C4.5) may be misleading.
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Due to the obvious importance of accuracy in the performance of intrusion detection system, in addition to the algorithms used there is an increasing need for more activities to be carried out, aiming for improved accuracy and reduced real time used in detection. This paper investigates the use of filtered dataset on the performance of J48 Decision Tree classifier in its classification of a connection as either normal or an attack. The reduced dataset is based on using Gain Ratio attribute evaluation technique (entropy) for performing feature selection (removal of redundant attributes) and feeding the filtered dataset into a J48 Decision Tree algorithm for classification. A 10-fold cross validationtechnique was used for the performance evaluation of the J48 Decision Tree classifier on the KDD cup 1999 dataset and simulated in WEKA tool. The results showed J48 decision tree algorithm performed better in terms of accuracy and false positive report on the reduced dataset than the full dataset(Probing full dataset: 97.8%, Probing reduced dataset: 99.5%, U2R full dataset: 75%, reduced dataset: 76.9%, R2L full dataset: 98.0%, reduced dataset: 98.3%).
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This research work manages productive data mining methodology for predicting the dengue from medicinal records of patients. Dengue is an extremely regular disease nowadays in all populations and in all age gatherings. Dengue to coronary disease and builds the dangers of creating kidney ailment, nerve harm, vein harm and visual deficiency. So mining the dengue data in a productive way is a basic issue. The Dengue Dataset collected on Krishnagiri district Government Hospital is utilized as a part of this paper; which gathers the data of patients with and without having dengue. The modified J48 classifier is utilized to build the precision rate of the data mining system. The data mining tool WEKA has been utilized for creating the modified J48 classifiers. Exploratory outcomes demonstrated a noteworthy change over the current J48 algorithm [1].
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Objective : To focus on classification algorithms to arrive better prediction model for Lung Disease Severity. Methods/Statistical analysis : In therapeutic analyses, the part of information mining methodologies is being expanded. Especially Classification calculations are exceptionally useful in arranging the information, which is critical for basic leadership prepare for therapeutic experts. In this paper the analysis is done in the WEKA apparatus on the spiro informational index. Findings : The paper embarks to make relative assessment of classifiers, for example, J48, Random forest and proposed Hybird Decision Tree(HDT) Algorithm with regards to Spiro dataset to amplify genuine positive rate and limit false positive rate of defaulters as opposed to accomplishing just higher grouping exactness utilizing WEKA instrument. The tests comes about appeared in this paper are about grouping exactness, affectability and specificity. Application/Improvements : The outcomes created on this dataset likewise demonstrate that the productivity and exactness of J48 is superior to anything other choice tree classifiers. J48 develops purge branches, it is the most urgent stride for govern era in J48. In more often than not this approach over fits the preparation cases with boisterous information. The proposed Hybird Decision Tree (HDT) Algorithm demonstrates great exactness in less time.
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In the field of goods production, demand prediction is important. By doing sales predictions, companies can make calculations and forecasts for what raw materials are mostly ordered. J48 and Naïve Bayes algorithm are two popular machine learning technique. By using these two algorithms, this study aims to develop an accurate and more reliable predictive model that help the company to make data driven decision. This study focuses on the application of quantitative methods, specifically the J48 algorithm and Naïve Bayes algorithm. This research conducted 4 times testing on each algorithm. This study produces high accuracy values with the Naïve Bayes and J48 algorithms. Both algorithm results have a fairly high accuracy value of 94% for Naïve Bayes and 98% for J48. The findings of this study implicate that by using J48 and Naïve Bayes algorithm, company can make informed decisions lead to improved operational efficiency, cost-effective, and resource utilization.
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