scholarly journals A Comparative Study between the K-Nearest Neighbors and the Multi-Layer Perceptron for Cursive Handwritten Arabic Numerals Recognition

2014 ◽  
Vol 107 (21) ◽  
pp. 25-30
Author(s):  
B. ElKessab ◽  
C. Daoui ◽  
B. Boukhalene ◽  
R. Salouan
Keyword(s):  
Comparative Study ◽  
Nearest Neighbors ◽  
Multi Layer Perceptron ◽  
K Nearest Neighbors ◽  
Handwritten Arabic

scholarly journals Using Linear Discriminant Analysis for Dimensionality Reduction for Predicting Anomalies of BGP data

2019 ◽  
Vol 8 (11) ◽  
pp. 1989-1995
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
High Dimensions ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Data Packets ◽  
Vector Machines

Border Gateway Protocol (BGP) is a vital protocol on the internet for transfer of data packets among Autonomous System (AS). Security is a major concern for the transmission of BGP packets which are often attacked by worms or are hijacked by an attacker which results in requests entering black holes or loss of connection to the particular sites. The BGP anomalies can be reduced by analyzing the BGP datasets. Since, ASes communicate through messages, therefore, the anomalies can be reduced by identifying the corrupted BGP message in the dataset. In this paper, BGP anomalies have been classified by applying Machine learning (ML) algorithms. The dataset contains information about the sending and receiving time between ASes. The classifiers were used to predict the anomalies. Since the dataset had high dimensions, the dimensions were reduced using Linear Discriminant Analysis (LDA) and then Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Linear Regression, Logistic Regression and Multi-Layer Perceptron (MLP) have been used to classify the anomalies.

scholarly journals Development and research of algorithms for determining user preferred public transport stops in a geographic information system based on machine learning methods

2020 ◽  
Vol 44 (4) ◽  
pp. 646-652
Author(s):  
A.A. Borodinov
Keyword(s):  
Machine Learning ◽  
Public Transport ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
Calculation Algorithm ◽  
Learning Methods ◽  
K Nearest Neighbors ◽  
Vector Method ◽  
Machine Learning Methods

The paper considers a problem of determining the user preferred stops in a public transport recommender system. The effectiveness of using various machine learning methods to solve this problem in a system of personalized recommendations is compared, including a support vector method, a decision tree, a random forest, AdaBoost, a k-nearest neighbors algorithm, and a multi-layer perceptron. The described traditional methods of machine learning are also compared with the method proposed herein and based on an estimate calculation algorithm. The efficiency and the effectiveness of the proposed method are confirmed in the work.

scholarly journals ỨNG DỤNG CÁC THUẬT TOÁN HỌC MÁY ĐỂ ĐÁNH GIÁ BỘ CƠ SỞ DỮ LIỆU TRONG PHÂN LOẠI RỐI LOẠN PHỔ TỰ KỶ

2020 ◽  
Vol 10 (3) ◽  
pp. 39
Author(s):  
Phạm Quang Thuận ◽  
Nguyễn Đình Thuân
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Decision Trees ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Naïve Bayes ◽  
Multi Layer Perceptron ◽  
K Nearest Neighbors

Bài báo này, chúng tôi trình bày kết quả đánh giá bộ cơ sở dữ liệu trong phân loại rối loạn phổ tự kỷ (ASD) trẻ em trên kho dữ liệu UCI. Chúng tôi tiến hành đánh giá bộ dữ liệu với các thuật toán SVM và Random Forest, đồng thời khảo sát thêm các thuật toán Decision Trees, Logistic Regression, K-Nearest-Neighbors, Naïve Bayes, và mạng nơ-ron Multi Layer Perceptron (MLP). Kết quả thử nghiệm trên bảy thuật toán cho kết quả phân loại cao phù hợp với các nghiên cứu trước đó. Chúng tôi kết luận bộ dữ liệu phân loại rối loạn phổ tự kỷ trẻ em trên kho dữ liệu UCI là đáng tin cậy.

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