scholarly journals Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management

2021 ◽  
Vol 11 (12) ◽  
pp. 5533
Author(s):  
Jui-Sheng Chou ◽  
Trang Thi Phuong Pham ◽  
Chia-Chun Ho
Keyword(s):  
Binary Classification ◽  
Learning System ◽  
Steel Plates ◽  
Support Vector ◽  
Classification Learning ◽  
Urban Land Cover ◽  
Quality Of Water ◽  
Multi Level ◽  
Multi Class Classification

Multi-class classification is one of the major challenges in machine learning and an ongoing research issue. Classification algorithms are generally binary, but they must be extended to multi-class problems for real-world application. Multi-class classification is more complex than binary classification. In binary classification, only the decision boundaries of one class are to be known, whereas in multiclass classification, several boundaries are involved. The objective of this investigation is to propose a metaheuristic, optimized, multi-level classification learning system for forecasting in civil and construction engineering. The proposed system integrates the firefly algorithm (FA), metaheuristic intelligence, decomposition approaches, the one-against-one (OAO) method, and the least squares support vector machine (LSSVM). The enhanced FA automatically fine-tunes the hyperparameters of the LSSVM to construct an optimized LSSVM classification model. Ten benchmark functions are used to evaluate the performance of the enhanced optimization algorithm. Two binary-class datasets related to geotechnical engineering, concerning seismic bumps and soil liquefaction, are then used to clarify the application of the proposed system to binary problems. Further, this investigation uses multi-class cases in civil engineering and construction management to verify the effectiveness of the model in the diagnosis of faults in steel plates, quality of water in a reservoir, and determining urban land cover. The results reveal that the system predicts faults in steel plates with an accuracy of 91.085%, the quality of water in a reservoir with an accuracy of 93.650%, and urban land cover with an accuracy of 87.274%. To demonstrate the effectiveness of the proposed system, its predictive accuracy is compared with that of a non-optimized baseline model, single multi-class classification algorithms (sequential minimal optimization (SMO), the Multiclass Classifier, the Naïve Bayes, the library support vector machine (LibSVM) and logistic regression) and prior studies. The analytical results show that the proposed system is promising project analytics software to help decision makers solve multi-level classification problems in engineering applications.

scholarly journals Multi-Classification of Fetal Health Status Using Extreme Learning Machine

2021 ◽  
Vol 5 (2) ◽  
pp. 62-70
Author(s):  
Ömer KASIM
Keyword(s):  
Extreme Learning Machine ◽  
Classification Accuracy ◽  
Binary Classification ◽  
Clinical Decision ◽  
Support Vector ◽  
Data Set ◽  
Multiple Classification ◽  
Learning Machine ◽  
Multi Class Classification ◽  
The University

Cardiotocography (CTG) is used for monitoring the fetal heart rate signals during pregnancy. Evaluation of these signals by specialists provides information about fetal status. When a clinical decision support system is introduced with a system that can automatically classify these signals, it is more sensitive for experts to examine CTG data. In this study, CTG data were analysed with the Extreme Learning Machine (ELM) algorithm and these data were classified as normal, suspicious and pathological as well as benign and malicious. The proposed method is validated with the University of California International CTG data set. The performance of the proposed method is evaluated with accuracy, f1 score, Cohen kappa, precision, and recall metrics. As a result of the experiments, binary classification accuracy was obtained as 99.29%. There was only 1 false positive.  When multi-class classification was performed, the accuracy was obtained as 98.12%.  The amount of false positives was found as 2. The processing time of the training and testing of the ELM algorithm were quite minimized in terms of data processing compared to the support vector machine and multi-layer perceptron. This result proved that a high classification accuracy was obtained by analysing the CTG data both binary and multiple classification.

Compactness Inspection Methods for Epoxy-Bonded Steel Plates of Bridge Strengthening

2011 ◽  
Vol 94-96 ◽  
pp. 1313-1317
Author(s):  
Jun Xie ◽  
Guo Liang Wang ◽  
Xiao Hua Zheng ◽  
Yi Shu Zhou
Keyword(s):  
Critical Evaluation ◽  
Steel Plates ◽  
Theory Analysis ◽  
Construction Quality ◽  
Bridge Strengthening ◽  
Multi Level ◽  
Practical Validation

Construction quality of epoxy-bonded steel plates is not easy to check for absence of inspection standard in situ. In this paper inspection methods of the critical evaluation factor-bond compactness of epoxy- bonded steel plates, hammer test based multi-level grids and infrared holography, are proposed by theory analysis ,specimen experiments and practical validation in engineering. In final he suggestion on the practical usage of these two methods are also presented.

Recommender System Techniques and Approaches to Improve the Modern Learning Challenges

2021 ◽  
pp. 114-143
Author(s):  
Aravindha Ramanan S.
Keyword(s):  
Recommender System ◽  
Recommendation Systems ◽  
Learning System ◽  
Learning Challenges ◽  
Technological Advances ◽  
Training Professionals ◽  
Modern Learning ◽  
Multi Level ◽  
The Web

Recommendation systems have been developed from the web. These recommendation systems are useful in collecting information from an available set of sources for a user's preferences. The information can be acquired from user's collection of details to share, to review, to do positive ratings by monitoring the user's behavior to improve the quality of top ‘N' recommendations. Now if we come to modern learning system, it has good framework to influence the training factors from the data, triggers, and learner's preferences. Modern learning can be compared to online learning which carries to the future needs. Modern learning can be instituted in schools, engineering colleges, and working campus. The modern learning system combines interrelated data, processes, and resources to create a system of interdependencies that work together, adapting to changing business needs. These interdependencies include multi-level dynamics driven by the organization, training professionals, technological advances, and the learners themselves.

DECISION TREE SUPPORT VECTOR MACHINE

2007 ◽  
Vol 16 (01) ◽  
pp. 1-15 ◽  
Author(s):  
LI ZHANG ◽  
WEI-DA ZHOU ◽  
TIAN-TIAN SU ◽  
LI-CHENG JIAO
Keyword(s):  
Decision Tree ◽  
Clustering Algorithm ◽  
Binary Classification ◽  
Classification Problem ◽  
Support Vector ◽  
Binary Decision ◽  
Binary Decision Tree ◽  
Kernel Clustering ◽  
Binary Classification Problem ◽  
Multi Class Classification

A new multi-class classifier, decision tree SVM (DTSVM) which is a binary decision tree with a very simple structure is presented in this paper. In DTSVM, a problem of multi-class classification is decomposed into a series of ones of binary classification. Here, the binary decision tree is generated by using kernel clustering algorithm, and each non-leaf node represents one binary classification problem. By compared with the other multi-class classification methods based on the binary classification SVMs, the scale and the complexity of DTSVM are less, smaller number of support vectors are needed, and has faster test speed. The final simulation results confirm the feasibility and the validity of DTSVM.

scholarly journals Text Classification by Genre Based on Rhythm Features

2021 ◽  
Vol 28 (3) ◽  
pp. 280-291
Author(s):  
Ksenia Vladimirovna Lagutina ◽  
Nadezhda Stanislavovna Lagutina ◽  
Elena Igorevna Boychuk
Keyword(s):  
Binary Classification ◽  
Software Tool ◽  
Statistical Features ◽  
Good Marker ◽  
Text Corpora ◽  
Text Model ◽  
Multi Class Classification ◽  
Almost All

The article is devoted to the analysis of the rhythm of texts of different genres: fiction novels, advertisements, scientific articles, reviews, tweets, and political articles. The authors identified lexico-grammatical figures in the texts: anaphora, epiphora, diacope, aposiopesis, etc., that are markers of the text rhythm. On their basis, statistical features were calculated that describe quantitatively and structurally these rhythm features.The resulting text model was visualized for statistical analysis using boxplots and heat maps that showed differences in the rhythm of texts of different genres. The boxplots showed that almost all genres differ from each other in terms of the overall density of rhythm features. Heatmaps showed different rhythm patterns across genres. Further, the rhythm features were successfully used to classify texts into six genres. The classification was carried out in two ways: a binary classification for each genre in order to separate a particular genre from the rest genres, and a multi-class classification of the text corpus into six genres at once. Two text corpora in English and Russian were used for the experiments. Each corpus contains 100 fiction novels, scientific articles, advertisements and tweets, 50 reviews and political articles, i.e. a total of 500 texts. The high quality of the classification with neural networks showed that rhythm features are a good marker for most genres, especially fiction. The experiments were carried out using the ProseRhythmDetector software tool for Russian and English languages. Text corpora contains 300 texts for each language.

Multiple sub-hyper-spheres support vector machine for multi-class classification

2014 ◽  
Vol 12 (03) ◽  
pp. 1450035 ◽  
Author(s):  
Shuang Liu ◽  
Peng Chen ◽  
Keqiu Li
Keyword(s):  
Support Vector Machine ◽  
Binary Classification ◽  
Decision Rules ◽  
Classification Problem ◽  
Decision Function ◽  
Support Vector ◽  
Similarity Function ◽  
Data Set ◽  
Computation Cost ◽  
Multi Class Classification

Support vector machine (SVM) is originally proposed to solve binary classification problem. Multi-class classification is solved by combining multiple binary classifiers, which leads to high computation cost by introducing many quadratic programming (QP) problems. To decrease computation cost, hyper-sphere SVM is put forward to compute class-specific hyper-sphere for each class. If all resulting hyper-spheres are independent, all training and test samples can be correctly classified. When some of hyper-spheres intersect, new decision rules should be adopted. To solve this problem, a multiple sub-hyper-sphere SVM is put forward in this paper. New algorithm computed hyper-spheres by SMO algorithm for all classes first, and then obtained position relationships between hyper-spheres. If hyper-spheres belong to the intersection set, overlap coefficient is computed based on map of key value index and mother hyper-spheres are partitioned into a series of sub-hyper-spheres. For the new intersecting hyper-spheres, one similarity function or same error sub-hyper-sphere or different error sub-hyper-sphere are used as decision rule. If hyper-spheres belong to the inclusion set, the hyper-sphere with larger radius is partitioned into sub-hyper-spheres. If hyper-spheres belong to the independence set, a decision function is defined for classification. With experimental results compared to other hyper-sphere SVMs, our new proposed algorithm improves the performance of the resulting classifier and decreases computation complexity for decision on both artificial and benchmark data set.

scholarly journals An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 446
Author(s):  
Andrew Churcher ◽  
Rehmat Ullah ◽  
Jawad Ahmad ◽  
Sadaqat ur Rehman ◽  
Fawad Masood ◽  
...  
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Denial Of Service ◽  
Intrusion Detection Systems ◽  
Support Vector ◽  
Distributed Denial Of Service ◽  
Detection Systems ◽  
Iot Devices ◽  
Artificial Neural Network Ann ◽  
Multi Class Classification

In recent years, there has been a massive increase in the amount of Internet of Things (IoT) devices as well as the data generated by such devices. The participating devices in IoT networks can be problematic due to their resource-constrained nature, and integrating security on these devices is often overlooked. This has resulted in attackers having an increased incentive to target IoT devices. As the number of attacks possible on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to cope with these attacks efficiently. In this paper, we highlight several machine learning (ML) methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS. In this work, ML algorithms are compared for both binary and multi-class classification on Bot-IoT dataset. Based on several parameters such as accuracy, precision, recall, F1 score, and log loss, we experimentally compared the aforementioned ML algorithms. In the case of HTTP distributed denial-of-service (DDoS) attack, the accuracy of RF is 99%. Furthermore, other simulation results-based precision, recall, F1 score, and log loss metric reveal that RF outperforms on all types of attacks in binary classification. However, in multi-class classification, KNN outperforms other ML algorithms with an accuracy of 99%, which is 4% higher than RF.

Reflecting the Sky in Water

2017 ◽  
Vol 3 (1) ◽  
pp. 112-126 ◽  
Author(s):  
Ilaria Cristofaro
Keyword(s):  
The Unconscious ◽  
Related Case ◽  
Quality Of Water ◽  
Fluid Properties ◽  
Phenomenological Perspective ◽  
Auto Ethnography ◽  
The Individual ◽  
Artificial Water ◽  
Land Water

From a phenomenological perspective, the reflective quality of water has a visually dramatic impact, especially when combined with the light of celestial phenomena. However, the possible presence of water as a means for reflecting the sky is often undervalued when interpreting archaeoastronomical sites. From artificial water spaces, such as ditches, huacas and wells to natural ones such as rivers, lakes and puddles, water spaces add a layer of interacting reflections to landscapes. In the cosmological understanding of skyscapes and waterscapes, a cross-cultural metaphorical association between water spaces and the underworld is often revealed. In this research, water-skyscapes are explored through the practice of auto-ethnography and reflexive phenomenology. The mirroring of the sky in water opens up themes such as the continuity, delimitation and manipulation of sky phenomena on land: water spaces act as a continuation of the sky on earth; depending on water spaces’ spatial extension, selected celestial phenomena can be periodically reflected within architectures, so as to make the heavenly dimension easily accessible and a possible object of manipulation. Water-skyscapes appear as specular worlds, where water spaces are assumed to be doorways to the inner reality of the unconscious. The fluid properties of water have the visual effect of dissipating borders, of merging shapes, and, therefore, of dissolving identities; in the inner landscape, this process may represent symbolic death experiences and rituals of initiation, where the annihilation of the individual allows the creative process of a new life cycle. These contextually generalisable results aim to inspire new perspectives on sky-and-water related case studies and give value to the practice of reflexive phenomenology as crucial method of research.

Chất lượng nước ở Bắc Bình dựa trên các kết quả phân tích hóa học và vi sinh

2018 ◽  
Vol 28 (4) ◽  
pp. 484-497
Author(s):  
Phan Thị Kim Văn ◽  
Bùi Trần Vượng
Keyword(s):  
Quality Of Water ◽  
Microbiological Analyses

The quality of water in Bac Binh according to chemical and microbiological analyses

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

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