scholarly journals PredictingMycobacterium tuberculosisComplex Clades Using Knowledge-Based Bayesian Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Minoo Aminian ◽  
David Couvin ◽  
Amina Shabbeer ◽  
Kane Hadley ◽  
Scott Vandenberg ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Dna Fingerprint ◽  
Support Vector ◽  
Rule Based ◽  
Polyhedral Sets ◽  
Knowledge Based ◽  
Novel Approach ◽  
The World ◽  
Rule Set

We develop a novel approach for incorporating expert rules into Bayesian networks for classification ofMycobacterium tuberculosiscomplex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes), since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web.

scholarly journals Automating Terrain Texturing in Real-Time Using a Rule-Based Approach

2010 ◽  
Vol 9 (4) ◽  
pp. 21-28
Author(s):  
John Ferraris ◽  
Christos Gatzidis ◽  
Feng Tian
Keyword(s):  
Real Time ◽  
Rule Based ◽  
Novel Approach ◽  
Great Performance ◽  
Rule Set ◽  
Complex Features ◽  
Rule Based Approach

This publication proposes a novel approach to automatically colour and texture a given terrain mesh in real time. Through the use of weighting rules, a simple syntax allows for the generation of texture and colour values based on the elevation and angle of a given vertex. It is through this combination of elevation and angle that complex features such as ridges, hills and mountains can be described, with the mesh coloured and textured accordingly. The implementation of the approach is done entirely on the GPU using 2D lookup textures, delivering a great performance increase over typical approaches that pass colour and weighting information in the fragment shader. In fact, the rule set is abstracted enough to be used in conjunction with any colouring/texturing approach that uses weighting values to dictate which surfaces are depicted on the mesh

A Novel Approach for Dimensionality Reduction and Classification of Big Data Using Meta-Heuristic Approach

2020 ◽  
Vol 17 (11) ◽  
pp. 5182-5197
Author(s):  
Amrinder Kaur ◽  
Rakesh Kumar
Keyword(s):  
Big Data ◽  
User Interaction ◽  
Extraction Process ◽  
Support Vector ◽  
Novel Approach ◽  
Whale Optimization ◽  
Distribution Index ◽  
Artificial Neural Network Ann ◽  
Feature Selection Approach

User interaction over the internet is growing day by day. The social network users send massive information to the network to share with others on the network. This increases the information on social media, hence needed a mechanism to handle or manage such high dimensional data termed as Big Data. Big Data reduction can be performed by using a feature selection approach. But, the Classification of such massive data is a challenging task for all the researchers. To overcome this problem, a metaheuristic based Genetic Algorithm (GA) for the selection of most suitable rows which can be provided for training. The selected rows undergo a feature extraction process, which is attained by Principle Component Analysis (PCA). The extracted principle components are optimized using another meta-heuristic algorithm termed as Whale Optimization. As the proposed algorithm uses unlabelled data, clustering is done to label the data. Two different distribution indexes were calculated for data with GA selected rows and data with GA selected rows along with PCA and whale. The distribution index is the ratio of a total number of elements in one cluster to a total number of elements in the second cluster. High distribution index leads to better accuracy when it comes to classifying the text data. The data is clustered using the K-Means algorithm to find the cluster indexes. The proposed algorithm presents a hybrid classification mechanism with upper and lower boundaries of classified labels using Artificial Neural Network (ANN) and Support Vector Machine (SVM).

Sentiment classification of Chinese cosmetic reviews based on integration of collocations and concepts

2019 ◽  
Vol 38 (1) ◽  
pp. 155-169
Author(s):  
Chihli Hung ◽  
You-Xin Cao
Keyword(s):  
Design Methodology ◽  
Classification Performance ◽  
Sentiment Classification ◽  
Support Vector ◽  
Research Approach ◽  
Content Type ◽  
Novel Approach ◽  
The Relationship ◽  
Modelling Approach

Purpose This paper aims to propose a novel approach which integrates collocations and domain concepts for Chinese cosmetic word of mouth (WOM) sentiment classification. Most sentiment analysis works by collecting sentiment scores from each unigram or bigram. However, not every unigram or bigram in a WOM document contains sentiments. Chinese collocations consist of the main sentiments of WOM. This paper reduces the complexity of the document dimensionality and makes an improvement for sentiment classification. Design/methodology/approach This paper builds two contextual lexicons for feature words and sentiment words, respectively. Based on these contextual lexicons, this paper uses the techniques of associated rules and mutual information to build possible Chinese collocation sets. This paper applies preference vector modelling as the vector representation approach to catch the relationship between Chinese collocations and their associated concepts. Findings This paper compares the proposed preference vector models with benchmarks, using three classification techniques (i.e. support vector machine, J48 decision tree and multilayer perceptron). According to the experimental results, the proposed models outperform all benchmarks evaluated by the criterion of accuracy. Originality/value This paper focuses on Chinese collocations and proposes a novel research approach for sentiment classification. The Chinese collocations used in this paper are adaptable to the content and domains. Finally, this paper integrates collocations with the preference vector modelling approach, which not only achieves a better sentiment classification performance for Chinese WOM documents but also avoids the curse of dimensionality.

Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers

2020 ◽  
Vol 20 (04) ◽  
pp. 2050035
Author(s):  
Sumit Dhariwal ◽  
Sellappan Palaniappan
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Error Rate ◽  
Kernel Functions ◽  
Support Vector ◽  
Image Normalization ◽  
Novel Approach ◽  
Low Levels ◽  
To Come

The content of massive image changing the brightest brightness is an impasse between most tests of sorted image realizations with low-resolution representation. I have done this research through image security, which will help curb crime in the coming days, and we propose a novel receipt for their strong and effective counterpart. Image classification using low levels of the image is a difficult method, so for this, I have adopted the method of automating the semantic image classification of this research and used it with different SVM classifiers, based on the normalized weighted feature support vector machine for semantic image classification. This is a novel approach given that weighted feature or normalized biased feature is applied and it is found that the normalized method is the best. It also uses normalized weighted features to compute kernel functions and train SVM. The trained SVM is then used to classify new images. During training and generalization, we displayed a decrease of identification error rate and there have been many benefits of using SVM with better performance in normalized image-cataloging systems. The importance of this technique and its role will be highlighted in the years to come.

scholarly journals Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification

2019 ◽  
Vol 9 (15) ◽  
pp. 3135 ◽  
Author(s):  
Mrinmoy Sarkar ◽  
Dhiman Chowdhury ◽  
Celia Shahnaz ◽  
Shaikh Anowarul Fattah
Keyword(s):  
Power Distribution ◽  
Performance Evaluations ◽  
Classification Model ◽  
Electrical Network ◽  
Support Vector ◽  
Distribution Grid ◽  
Audio Signals ◽  
Novel Approach ◽  
The World ◽  
Digital Recordings

Electrical network frequency (ENF) is a signature of a power distribution grid. It represents the deviation from the nominal frequency (50 or 60 Hz) of a power system network. The variations in ENF sequences within a grid are subject to load fluctuations within that particular grid. These ENF variations are inherently located in a multimedia signal, which is recorded close to the grid or directly from the mains power line. Thus, the specific location of a recording can be identified by analyzing the ENF sequences of the multimedia signal in absence of the concurrent power signal. In this article, a novel approach to location-stamp authentication based on ENF sequences of digital recordings is presented. ENF patterns are extracted from a number of power and audio signals recorded in different grid locations across the world. The extracted ENF signals are decomposed into low outliers and high outliers frequency segments and potential feature vectors are determined for these ENF segments by statistical and signal processing analysis. Then, a multi-class support vector machine (SVM) classification model is developed to verify the location-stamp information of the recordings. The performance evaluations corroborate the efficacy of the proposed framework.

scholarly journals The Influence of Classification on World View and Epistemology

10.28945/3279 ◽  
2008 ◽  
Author(s):  
Gholamreza Fadaie
Keyword(s):  
Classification Scheme ◽  
World View ◽  
Point Of View ◽  
Knowledge Based ◽  
Key Points ◽  
The World ◽  
Static Approach ◽  
Traditional Approaches ◽  
Knowledge Classification

Worldview as a kind of man's look towards the world of reality has a severe influence on his classification of knowledge. In other words one may see in classification of knowledge the unity as well as plurality. This article deals with the fact that how classification takes place in man's epistemological process. Perception and epistemology are mentioned as the key points here. Philosophers are usually classifiers and their point of views forms the way they classify things and concepts. Relationship and how one looks at it in shaping the classification scheme is critical. The classifications which have been introduced up to now have had several models. They represent the kind of looking at, or point of view of their founders to the world. Aristotle, as a philosopher as well as an encyclopedist, is one of the great founders of knowledge classification. Afterwards the Islamic scholars followed him while some few rejected his model and made some new ones. If we divide all classifications according to their roots we may define them as human based classification, theology based classification, knowledge based classification, materialistic based classification such as Britannica's classification, and fact based classification. Tow broad approaches have been defined in this article: static and dynamic. The static approach refers to the traditional approaches and the dynamic one refers to the eight way of looking toward objects in order to realize them. The structure of classification has had its influence on epistemology, too. If the first cut on knowledge tree is fully defined, the branches would usually be consistent with it.

scholarly journals A Novel Approach of Frequent Itemsets Mining for Coronavirus Disease (COVID-19)

2021 ◽  
Vol 5 (2) ◽  
pp. 5-12
Author(s):  
Mai Shawkat ◽  
Mahmoud Badawi ◽  
Ali I. Eldesouky
Keyword(s):  
Frequent Itemsets ◽  
Rule Mining ◽  
Global Pandemic ◽  
Novel Approach ◽  
Antibiotic Drugs ◽  
The World ◽  
Potential Applications ◽  
Medical Reports ◽  
Frequent Itemsets Mining

The global pandemic of new coronaviruses (COVID-19) has infected many people around the world and became a worldwide concern since this disease caused illness and deaths. The vaccine and drugs are not scientifically established, but patients are recovering with antibiotic drugs, antiviral medicine, chloroquine, and vitamin C. Now it is obvious to the world that a quicker and faster solution is needed for monitoring and combating the further spread of COVID-19 worldwide, using non-clinical techniques, for example, data mining tools, enhanced intelligence, and other artificial intelligence technologies. In this paper, association rule mining is developing for the frequent itemsets discovery in COVID-19 datasets, and the extraction of effective association relations between them. This is done by demonstrates the analysis of the Coronavirus dataset by using the Apriori_Association_Rules algorithm. It involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. The major contribution of this study employment determines the effectiveness of the Apriori_Association_Rules algorithm towards a classification of medical reports. The experimental results provide evidence of the Apriori_Association_Rules algorithm regarding the execution time, memory consumption, and several associated rules that reflect its potential applications to different contexts. Therefore, the Apriori_Association_Rules algorithm will be very useful in healthcare fields to demonstrate the latest developments in medical studies fighting COVID-19.

scholarly journals Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Yi-Li Tseng ◽  
Keng-Sheng Lin ◽  
Fu-Shan Jaw
Keyword(s):  
Support Vector Machine ◽  
Myocardial Ischemia ◽  
Sparse Representation ◽  
High Performance ◽  
Feature Space ◽  
Support Vector ◽  
Classification Methods ◽  
Rule Based ◽  
Knowledge Based ◽  
Coronary Events

An automatic method is presented for detecting myocardial ischemia, which can be considered as the early symptom of acute coronary events. Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals. The methods in this study are proposed to detect abnormal ECG beats using knowledge-based features and classification methods. A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms. A comparison was made between two classification methods, SRC and support-vector machine (SVM), using rule-based vectors as input feature space. The two methods are proposed with quantitative evaluation to validate their performances. The results of SRC method encompassed with rule-based features demonstrate higher sensitivity than that of SVM. However, the specificity and precision are a trade-off. Moreover, SRC method is less dependent on the selection of rule-based features and can achieve high performance using fewer features. The overall performances of the two methods proposed in this study are better than the previous methods.

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