An optimized Parkinson's disorder identification through evolutionary fast learning network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bouslah Ayoub ◽  
Taleb Nora
Keyword(s):  
Feature Model ◽  
Classification Model ◽  
Support Vector ◽  
High Exposure ◽  
Content Type ◽  
Superior Result ◽  
Learning Network ◽  
Fast Learning ◽  
Proposed Model ◽  
Fast Learning Network

PurposeParkinson's disease (PD) is a well-known complex neurodegenerative disease. Typically, its identification is based on motor disorders, while the computer estimation of its main symptoms with computational machine learning (ML) has a high exposure which is supported by researches conducted. Nevertheless, ML approaches required first to refine their parameters and then to work with the best model generated. This process often requires an expert user to oversee the performance of the algorithm. Therefore, an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approachTo provide an available identification model for Parkinson disease as an auxiliary function for clinicians, the authors suggest a new evolutionary classification model. The core of the prediction model is a fast learning network (FLN) optimized by a genetic algorithm (GA). To get a better subset of features and parameters, a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.FindingsThe proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets. The very popular wrappers induction models such as support vector machine (SVM), K-nearest neighbors (KNN) have been tested in the same condition. The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/valueA novel efficient PD detection model is proposed, which is called A-W-FLN. The A-W-FLN utilizes FLN as the base classifier; in order to take its higher generalization ability, and identification capability is also embedded to discover the most suitable feature model in the detection process. Moreover, the proposed method automatically optimizes the FLN's architecture to a smaller number of hidden nodes and solid connecting weights. This helps the network to train on complex PD datasets with non-linear features and yields superior result.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Sales Growth Rate Forecasting Using Improved PSO and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xibin Wang ◽  
Junhao Wen ◽  
Shafiq Alam ◽  
Xiang Gao ◽  
Zhuo Jiang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Decisive Role ◽  
Convergence Speed ◽  
Classification Model ◽  
Support Vector ◽  
Local Optimum ◽  
Sales Growth ◽  
Local Optima ◽  
Proposed Model ◽  
Forecasting Performance

Accurate forecast of the sales growth rate plays a decisive role in determining the amount of advertising investment. In this study, we present a preclassification and later regression based method optimized by improved particle swarm optimization (IPSO) for sales growth rate forecasting. We use support vector machine (SVM) as a classification model. The nonlinear relationship in sales growth rate forecasting is efficiently represented by SVM, while IPSO is optimizing the training parameters of SVM. IPSO addresses issues of traditional PSO, such as relapsing into local optimum, slow convergence speed, and low convergence precision in the later evolution. We performed two experiments; firstly, three classic benchmark functions are used to verify the validity of the IPSO algorithm against PSO. Having shown IPSO outperform PSO in convergence speed, precision, and escaping local optima, in our second experiment, we apply IPSO to the proposed model. The sales growth rate forecasting cases are used to testify the forecasting performance of proposed model. According to the requirements and industry knowledge, the sample data was first classified to obtain types of the test samples. Next, the values of the test samples were forecast using the SVM regression algorithm. The experimental results demonstrate that the proposed model has good forecasting performance.

A Supervised Learning Model for AGV Perception in Unstructured Environment

2015 ◽  
pp. 334-353
Author(s):  
Rizwan Aqeel ◽  
Saif Ur Rehman ◽  
Saira Gillani ◽  
Sohail Asghar
Keyword(s):  
Clustering Algorithm ◽  
Research Problem ◽  
Classification Model ◽  
Support Vector ◽  
Ground Vehicle ◽  
Second Stage ◽  
Initial Stage ◽  
Cluster Area ◽  
Proposed Model ◽  
Unstructured Environment

This chapter focuses on an Autonomous Ground Vehicle (AGV), also known as intelligent vehicle, which is a vehicle that can navigate without human supervision. AGV navigation over an unstructured road is a challenging task and is known research problem. This chapter is to detect road area from an unstructured environment by applying a proposed classification model. The Proposed model is sub divided into three stages: (1) - preprocessing has been performed in the initial stage; (2) - road area clustering has been done in the second stage; (3) - Finally, road pixel classification has been achieved. Furthermore, combination of classification as well as clustering is used in achieving our goals. K-means clustering algorithm is used to discover biggest cluster from road scene, second big cluster area has been classified as road or non road by using the well-known technique support vector machine. The Proposed approach is validated from extensive experiments carried out on RGB dataset, which shows that the successful detection of road area and is robust against diverse road conditions such as unstructured nature, different weather and lightening variations.

Adjustment mode decision based on support vector data description and evidence theory for assembly lines

2018 ◽  
Vol 118 (8) ◽  
pp. 1711-1726 ◽  
Author(s):  
Youlong Lv ◽  
Wei Qin ◽  
Jungang Yang ◽  
Jie Zhang
Keyword(s):  
Assembly Line ◽  
Historical Data ◽  
Classification Model ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Assembly Lines ◽  
Content Type ◽  
Input Layer ◽  
Multi Classification

PurposeThree adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.Design/methodology/approachThe decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.FindingsThe proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.Practical implicationsThis research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.Originality/valueThe adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

scholarly journals The Classification Model for Cloud DDOS Attack

2019 ◽  
Vol 8 (12) ◽  
pp. 1261-1264
Keyword(s):  
Data Transfer ◽  
Detection System ◽  
Classification Model ◽  
Support Vector ◽  
Superior Result ◽  
Ddos Attack ◽  
Digital World ◽  
Network Intrusion ◽  
Ip Spoofing ◽  
Tree Algorithms

Cloud computing may be a tremendous territory, utilize the resources with expense viably. The service provider is to share the resources anywhere whenever. In any case, the network is that the most basic to accessing information within the cloud. The cloud malicious takes focal points whereas utilizing the cloud network. Intrusion Detection System (IDS) is perceptive the network and tells attacks. Distributed denials of service (DDOS) attack have solid result on the digital world.. To the extent digital attack is worried that it stops the normal working of the association by Internet protocol (IP) spoofing, data transfer capacity flood, expending memory resources and causes an immense misfortune. There has been a ton of related work which concentrated on dissecting the example of the DDOS attacks to shield users from them. This paper proposes the utilizing support vector machine, Neural Networks, and decision tree algorithms for foreseeing undesirable data's. In these algorithms are help us to beat the high false caution rate. The proposed work executed part utilizing the R tool to give a statistical report, which gives a superior result in little figuring time

Ensemble incremental deep multiple layer perceptron model – sentiment analysis application

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Renuka Devi D. ◽  
Sasikala S.
Keyword(s):  
Feature Selection ◽  
Support Vector ◽  
Data Sets ◽  
Nearest Neighbour ◽  
Swarm Optimization ◽  
Content Type ◽  
Twitter Data ◽  
Proposed Model ◽  
Hoeffding Tree ◽  
Selection Algorithms

Purpose The purpose of this paper is to enhance the accuracy of classification of streaming big data sets with lesser processing time. This kind of social analytics would contribute to society with inferred decisions at a correct time. The work is intended for streaming nature of Twitter data sets. Design/methodology/approach It is a demanding task to analyse the increasing Twitter data by the conventional methods. The MapReduce (MR) is used for quickest analytics. The online feature selection (OFS) accelerated bat algorithm (ABA) and ensemble incremental deep multiple layer perceptron (EIDMLP) classifier is proposed for Feature Selection and classification. Three Twitter data sets under varied categories are investigated (product, service and emotions). The proposed model is compared with Particle Swarm Optimization, Accelerated Particle Swarm Optimization, accelerated simulated annealing and mutation operator (ASAMO). Feature Selection algorithms and classifiers such as Naïve Bayes, support vector machine, Hoeffding tree and fuzzy minimal consistent class subset coverage with the k-nearest neighbour (FMCCSC-KNN). Findings The proposed model is compared with PSO, APSO, ASAMO. Feature Selection algorithms, and classifiers such as Naïve Bayes (NB), support vector machine (SVM), Hoeffding Tree (HT), and Fuzzy Minimal Consistent Class Subset Coverage with the K-Nearest Neighbour (FMCCSC-KNN). The outcome of the work has achieved an accuracy of 99%, 99.48%, 98.9% for the given data sets with the processing time of 0.0034, 0.0024, 0.0053, seconds respectively. Originality/value A novel framework is proposed for Feature Selection and classification. The work is compared with the authors’ previously developed classifiers with other state-of-the-art Feature Selection and classification algorithms.

Sign in / Sign up

Export Citation Format

Share Document