scholarly journals Fuzzy Aspect Based Opinion Classification System for Mining Tourist Reviews

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Muhammad Afzaal ◽  
Muhammad Usman ◽  
A. C. M. Fong ◽  
Simon Fong ◽  
Yan Zhuang
Keyword(s):  
Classification System ◽  
Classification Accuracy ◽  
Opinion Mining ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Aspect Extraction ◽  
Real World Datasets ◽  
Available Information ◽  
Typical User ◽  
User Review

Due to the large amount of opinions available on the websites, tourists are often overwhelmed with information and find it extremely difficult to use the available information to make a decision about the tourist places to visit. A number of opinion mining methods have been proposed in the past to identify and classify an opinion into positive or negative. Recently, aspect based opinion mining has been introduced which targets the various aspects present in the opinion text. A number of existing aspect based opinion classification methods are available in the literature but very limited research work has targeted the automatic aspect identification and extraction of implicit, infrequent, and coreferential aspects. Aspect based classification suffers from the presence of irrelevant sentences in a typical user review. Such sentences make the data noisy and degrade the classification accuracy of the machine learning algorithms. This paper presents a fuzzy aspect based opinion classification system which efficiently extracts aspects from user opinions and perform near to accurate classification. We conducted experiments on real world datasets to evaluate the effectiveness of our proposed system. Experimental results prove that the proposed system not only is effective in aspect extraction but also improves the classification accuracy.

scholarly journals Competitive Caching with Machine Learned Advice

2021 ◽  
Vol 68 (4) ◽  
pp. 1-25
Author(s):  
Thodoris Lykouris ◽  
Sergei Vassilvitskii
Keyword(s):  
Online Algorithms ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Poor Performance ◽  
Machine Learning Algorithms ◽  
Average Error ◽  
Generalization Error ◽  
Worst Case ◽  
Future Events ◽  
Real World Datasets

Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.

INTELLIGENT UPPER-LIMB PROSTHETIC CONTROL (iULP) WITH NOVEL FEATURE EXTRACTION METHOD FOR PATTERN RECOGNITION USING EMG

2021 ◽  
pp. 2150043
Author(s):  
SIDHARTH PANCHOLI ◽  
AMIT M. JOSHI
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Time Derivative ◽  
Machine Learning Algorithms ◽  
Prosthetic Control ◽  
Myoelectric Prosthesis ◽  
Feature Extraction Method ◽  
Similar Work ◽  
Emg Signal

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.

scholarly journals A Novel Fast Training Method for SVM and Its Application in Fault Diagnosis of Service Robot

2015 ◽  
Vol 11 (6) ◽  
pp. 4 ◽  
Author(s):  
Xianfeng Yuan ◽  
Mumin Song ◽  
Fengyu Zhou ◽  
Yugang Wang ◽  
Zhumin Chen
Keyword(s):  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Service Robot ◽  
Support Vector ◽  
Svm Classifier ◽  
Processing Unit ◽  
Training Method ◽  
Fast Training

Support Vector Machines (SVM) is a set of popular machine learning algorithms which have been successfully applied in diverse aspects, but for large training data sets the processing time and computational costs are prohibitive. This paper presents a novel fast training method for SVM, which is applied in the fault diagnosis of service robot. Firstly, sensor data are sampled under different running conditions of the robot and those samples are divided as training sets and testing sets. Secondly, the sampled data are preprocessed and the principal component analysis (PCA) model is established for fault feature extraction. Thirdly, the feature vectors are used to train the SVM classifier, which achieves the fault diagnosis of the robot. To speed up the training process of SVM, on the one hand, sample reduction is done using the proposed support vectors selection (SVS) algorithm, which can ensure good classification accuracy and generalization capability. On the other hand, we take advantage of the excellent parallel computing abilities of Graphics Processing Unit (GPU) to pre-calculate the kernel matrix, which avoids the recalculation during the cross validation process. Experimental results illustrate that the proposed method can significantly reduce the training time without decreasing the classification accuracy.

scholarly journals An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Muhammad Waqar ◽  
Hassan Dawood ◽  
Hussain Dawood ◽  
Nadeem Majeed ◽  
Ameen Banjar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Heart Attack ◽  
High Reliability ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Unequal Distribution ◽  
The Given

Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

scholarly journals Ethical Guidelines for Publishing in the Journal of Cachexia, Sarcopenia and Muscle Clinical Reports

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Stephan Von Haehling ◽  
Nicole Ebner ◽  
John E Morley ◽  
Andrew JS Coats ◽  
Stefan D Anker
Keyword(s):  
Conflicts Of Interest ◽  
Research Work ◽  
Ethics Committees ◽  
Institutional Review Boards ◽  
Original Research ◽  
Ethical Guidelines ◽  
Abstract Form ◽  
Institutional Review ◽  
Ethical Authorship ◽  
Available Information

AbstractThis article details the principles of ethical authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle Clinical Reports (JCSM Clinical Reports). At the time of submission to JCSM Clinical Reports, the corresponding author, on behalf of all co-authors, needs to certify adherence to these principles. The principles are obtained below: All authors listed on a manuscript considered for publication have approved its submission and (if accepted) publication as provided to JCSM Clinical Reports;No person having a right to be recognized as author has been omitted from the list of authors on the submitted manuscript;The submitted work is original and is neither under consideration elsewhere nor has it been published previously in whole or in part other than in abstract form;All authors certify that the work is original and does not contain excessive overlap with prior or contemporaneous publication elsewhere, and where the publication reports on cohorts, trials, or data that have been reported on before these other publications must be referenced;All original research work are approved by the relevant bodies such as institutional review boards or ethics committees;All conflicts of interest, financial or otherwise, that may affect the authors' ability to present data objectively, and relevant sources of funding have been duly declared in the manuscript;The manuscript in its published form will be maintained on the servers of JCSM Clinical Reports as a valid publication only as long as all statements in the guidelines on ethical publishing remain true; andIf any of the aforementioned statements ceases to be true, the authors have a duty to notify the Editors of JCSM Clinical Reports as soon as possible so that the available information regarding the published article can be updated and/or the manuscript can be withdrawn.

scholarly journals Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

2021 ◽  
Author(s):  
Rejith K.N ◽  
Kamalraj Subramaniam ◽  
Ayyem Pillai Vasudevan Pillai ◽  
Roshini T V ◽  
Renjith V. Ravi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Signal Frequency ◽  
Spectral Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Energy Entropy ◽  
Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

scholarly journals Design of Hybrid Method for Automatic Sentiment Classification System

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3616-3620
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Classification System ◽  
Scientific Community ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Learning Approach ◽  
Business World ◽  
Major Aspect ◽  
Machine Learning Approach

The Developing enthusiasm for the field of opinion mining and its applications in various regions of information and also, sociology has activated numerous researchers to investigate the field The chance to catch the opinion of the overall public about get-togethers, political developments, organization systems, advertising efforts, and item inclinations has raised expanding enthusiasm of both scientific community (as a result of the energizing open difficulties) and the business world (due to the wonderful advantages for promoting and money related market expectation). Today, sentiment analysis investigation has its applications in a few unique situations. There are a decent number of organizations, both huge and little scale, that focuses on opinions and sentiments as a major aspect of their central goal. This work introduces hybrid approach that includes lexicon based approach and machine learning approach for extracting aspects and sentiments

Sign in / Sign up

Export Citation Format

Share Document