scholarly journals Classification of Dengue Hemorrhagic Fever (DHF) Spread in Bandung using Hybrid Naïve Bayes, K-Nearest Neighbor, and Artificial Neural Network Methods

2021 ◽  
Vol 7 (1) ◽  
pp. 10-20
Author(s):  
Fatri Nurul Inayah ◽  
Sri Suryani Prasetiyowati ◽  
Yuliant Sibaroni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dengue Fever ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Hemorrhagic Fever ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Artificial Neural

Dengue fever is a dangerous disease caused by the dengue virus. One of the factors causing dengue fever is due to the place where you live in the tropics, so that cases of dengue fever in Indonesia, especially in the Bandung Regency area, will continue to show high numbers. Therefore, information is needed on the spread of this disease by requiring the accuracy and speed of diagnosis as early prevention. In terms of compiling this information, classification techniques can be done using a combination of methods Naïve Bayes, K-Nearest Neighbor(KNN), and Artificial Neural Network(ANN) to build predictions of the classification of dengue fever, and the data used in this Final Project are dataset affected by the spread of dengue fever in Bandung regency in the 2012-2018 period. The hybrid classifier results can improve accuracy with the voting method with an accuracy level of 90% in the classification of dengue fever.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

scholarly journals Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

2013 ◽  
Vol 20 (2) ◽  
pp. 263-272 ◽  
Author(s):  
A. Moosavian ◽  
H. Ahmadi ◽  
A. Tabatabaeefar ◽  
M. Khazaee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Journal Bearing ◽  
Journal Bearings ◽  
K Nearest Neighbor ◽  
Ic Engine ◽  
Vibration Signals ◽  
Artificial Neural

Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

scholarly journals Plant disease prediction using classification algorithms

2021 ◽  
Vol 10 (1) ◽  
pp. 257
Author(s):  
Maria Morgan ◽  
Carla Blank ◽  
Raed Seetan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nearest Neighbor ◽  
Disease Classification ◽  
Future Research ◽  
Support Vector ◽  
Classification Algorithms ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Artificial Neural

<p>This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measurements instead of images. A fungus and a plant dataset, which had many differences, were chosen so that the findings in this paper could be applied to future research for disease prediction and classification in a variety of datasets which contain raw measurements. A key difference between the two datasets, other than one being a fungus and one being a plant, is that the mushroom dataset is balanced and only contained two classes while the soybean dataset is imbalanced and contained eighteen classes. All six algorithms performed well on the mushroom dataset, while the Artificial Neural Network and k-Nearest Neighbor algorithms performed best on the soybean dataset. The findings of this paper can be applied to future research on disease classification and prediction in a variety of dataset types such as fungi, plants, humans, and animals.</p>

scholarly journals COMPARING NAIVE BAYES, K-NEAREST NEIGHBOR, AND NEURAL NETWORK CLASSIFICATION METHODS OF SEAT LOAD FACTOR IN LOMBOK OUTBOUND FLIGHTS

2019 ◽  
Vol 16 (2) ◽  
pp. 187
Author(s):  
Mega Luna Suliztia ◽  
Achmad Fauzan
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Load Factor ◽  
Classification Methods ◽  
Affecting Factors ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Ticket Prices

Classification is the process of grouping data based on observed variables to predict new data whose class is unknown. There are some classification methods, such as Naïve Bayes, K-Nearest Neighbor and Neural Network. Naïve Bayes classifies based on the probability value of the existing properties. K-Nearest Neighbor classifies based on the character of its nearest neighbor, where the number of neighbors=k, while Neural Network classifies based on human neural networks. This study will compare three classification methods for Seat Load Factor, which is the percentage of aircraft load, and also a measure in determining the profit of airline.. Affecting factors are the number of passengers, ticket prices, flight routes, and flight times. Based on the analysis with 47 data, it is known that the system of Naïve Bayes method has misclassifies in 14 data, so the accuracy rate is 70%. The system of K-Nearest Neighbor method with k=5 has misclassifies in 5 data, so the accuracy rate is 89%, and the Neural Network system has misclassifies in 10 data with accuracy rate 78%. The method with highest accuracy rate is the best method that will be used, which in this case is K-Nearest Neighbor method with success of classification system is 42 data, including 14 low, 10 medium, and 18 high value. Based on the best method, predictions can be made using new data, for example the new data consists of Bali flight routes (2), flight times in afternoon (2), estimate of passenger numbers is 140 people, and ticket prices is Rp.700,000. By using the K-Nearest Neighbor method, Seat Load Factor prediction is high or at intervals of 80% -100%.

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