To see the other types of publications on this topic, follow the link: Minimal Optimization (SMO) algorithm.
Journal articles on the topic 'Minimal Optimization (SMO) algorithm'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Minimal Optimization (SMO) algorithm.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Keerthi, S. S., S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. "Improvements to Platt's SMO Algorithm for SVM Classifier Design." Neural Computation 13, no. 3 (March 1, 2001): 637–49. http://dx.doi.org/10.1162/089976601300014493.
Full textAbstract:
This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.
2
Tian, Li Yan, and Xiao Guang Hu. "Method of Parallel Sequential Minimal Optimization for Fast Training Support Vector Machine." Applied Mechanics and Materials 29-32 (August 2010): 947–51. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.947.
Full textAbstract:
A fast training support vector machine using parallel sequential minimal optimization is presented in this paper. Up to now, sequential minimal optimization (SMO) is one of the major algorithms for training SVM, but it still requires a large amount of computation time for the large sample problems. Unlike the traditional SMO, the parallel SMO partitions the entire training data set into small subsets first and then runs multiple CPU processors to seal with each of the partitioned data set. Experiments show that the new algorithm has great advantage in terms of speediness when applied to problems with large training sets and high dimensional spaces without reducing generalization performance of SVM.
3
Knebel, Tilman, Sepp Hochreiter, and Klaus Obermayer. "An SMO Algorithm for the Potential Support Vector Machine." Neural Computation 20, no. 1 (January 2008): 271–87. http://dx.doi.org/10.1162/neco.2008.20.1.271.
Full textAbstract:
We describe a fast sequential minimal optimization (SMO) procedure for solving the dual optimization problem of the recently proposed potential support vector machine (P-SVM). The new SMO consists of a sequence of iteration steps in which the Lagrangian is optimized with respect to either one (single SMO) or two (dual SMO) of the Lagrange multipliers while keeping the other variables fixed. An efficient selection procedure for Lagrange multipliers is given, and two heuristics for improving the SMO procedure are described: block optimization and annealing of the regularization parameter ε. A comparison of the variants shows that the dual SMO, including block optimization and annealing, performs efficiently in terms of computation time. In contrast to standard support vector machines (SVMs), the P-SVM is applicable to arbitrary dyadic data sets, but benchmarks are provided against libSVM's ε-SVR and C-SVC implementations for problems that are also solvable by standard SVM methods. For those problems, computation time of the P-SVM is comparable to or somewhat higher than the standard SVM. The number of support vectors found by the P-SVM is usually much smaller for the same generalization performance.
4
Gadal, Saad, Rania Mokhtar, Maha Abdelhaq, Raed Alsaqour, Elmustafa Sayed Ali, and Rashid Saeed. "Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization." Electronics 11, no. 14 (July 10, 2022): 2158. http://dx.doi.org/10.3390/electronics11142158.
Full textAbstract:
Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms.
5
Zhao, Zhe, and Xiao Yu Li. "Study of Sequential Minimal Optimization Algorithm Type and Kernel Function Selection for Short-Term Load Forecasting." Applied Mechanics and Materials 329 (June 2013): 472–77. http://dx.doi.org/10.4028/www.scientific.net/amm.329.472.
Full textAbstract:
Short-term load forecasting is important for power system operation,including preparing plans for generation and supply, arranging the generator to set start or stop, coordinating thermal power units and hydropower units. Support vector machines have advantage in approximating any nonlinear function with arbitrary precision and modeling by studying history data. Based on SVM, this paper selects the sequential minimal optimization (SMO) algorithm to compute load forecasting, because SMO can avoid iterative, so as to short the running time. If we select different kernel functions and the SMO type in the computing process, we will receive different result. Though the analysis of results,the paper obtains the optimal solution in different accuracy or time requirements for short-term load forecasting. By a power plant data, respectively, it discusses from the weekly load forecasts and daily load forecast to play an empirical analysis. It concludes that the selection of ɛ-SVR type and the linear form kernel function is ideal for short-term load forecasting in a not strictly time limits. Otherwise, it will select others in different terms.
6
Panigrahi, Satya Sobhan, and Ajay Kumar Jena. "Optimization of Test Cases in Object-Oriented Systems Using Fractional-SMO." International Journal of Open Source Software and Processes 12, no. 1 (January 2021): 41–59. http://dx.doi.org/10.4018/ijossp.2021010103.
Full textAbstract:
This paper introduces the technique to select the test cases from the unified modeling language (UML) behavioral diagram. The UML behavioral diagram describes the boundary, structure, and behavior of the system that is fed as input for generating the graph. The graph is constructed by assigning the weights, nodes, and edges. Then, test case sequences are created from the graph with minimal fitness value. Then, the optimal sequences are selected from the proposed fractional-spider monkey optimization (fractional-SMO). The developed fractional-SMO is designed by integrating fractional calculus and SMO. Thus, the efficient test cases are selected based on the optimization algorithm that uses fitness parameters, like coverage and fault. Simulations are performed via five synthetic UML diagrams taken from the dataset. The performance of the proposed technique is computed using coverage and the number of test cases. The maximal coverage of 49 and the minimal number of test cases as 2,562 indicate the superiority of the proposed technique.
7
Glasmachers, Tobias, and Christian Igel. "Second-Order SMO Improves SVM Online and Active Learning." Neural Computation 20, no. 2 (February 2008): 374–82. http://dx.doi.org/10.1162/neco.2007.10-06-354.
Full textAbstract:
Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow online and active learning. Second, for large data sets, computing the exact SVM solution may be too time-consuming, and an efficient approximation can be preferable. The powerful LASVM iteratively approaches the exact SVM solution using sequential minimal optimization (SMO). It allows efficient online and active learning. Here, this algorithm is considerably improved in speed and accuracy by replacing the working set selection in the SMO steps. A second-order working set selection strategy, which greedily aims at maximizing the progress in each single step, is incorporated.
8
Wibowo, Agung. "Aplikasi Diagnosis Penyakit Kanker Payudara Menggunakan Algoritma Sequential Minimal Optimization." Jurnal Teknologi dan Sistem Komputer 5, no. 4 (October 29, 2017): 153. http://dx.doi.org/10.14710/jtsiskom.5.4.2017.153-158.
Full textAbstract:
Various methods for the diagnosis of breast cancer exist, but not many have been implemented as an application. This study aims to develop an application using SMO algorithm assisted by Weka to diagnose breast cancer. The application was web-based application and developed using Javascript. Test dataset and model formation used original Breast Cancer Database (WBCD) data without missing value. Test mode used 10-fold cross-validation. This application can diagnose breast cancer with an accuracy of 97.3645% and has a significant increase in accuracy for the diagnosis of malignant cancer.Beragam metode untuk diagnosis kanker payudara, namun belum banyak yang diimplementasikan menjadi sebuah aplikasi. Penelitian ini bertujuan untuk mengembangkan aplikasi berdasarkan model hasil kalkulasi algoritma SMO berbantuan Weka untuk mendiagnosis penyakit kanker payudara. Aplikasi dikembangkan berbasis web menggunakan Javascript. Dataset pengujian dan pembentukan model menggunakan data Winconsin Breast Cancer Database original (WBCD) tanpa nilai hilang. Mode pengujian menggunakan 10-fold cross validation. Aplikasi ini dapat mendiagnosis kanker payudara dengan akurasi 97.3645% dan memiliki peningkatan akurasi yang signifikan untuk diagnosis kanker ganas.
9
Shao, Xigao, Kun Wu, and Bifeng Liao. "Single Directional SMO Algorithm for Least Squares Support Vector Machines." Computational Intelligence and Neuroscience 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/968438.
Full textAbstract:
Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs). In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO-) type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones.
10
Al-Ibrahim, Ali Mohammad H. "Using Sequential Minimal Optimization for Phishing Attack Detection." Modern Applied Science 13, no. 5 (April 30, 2019): 114. http://dx.doi.org/10.5539/mas.v13n5p114.
Full textAbstract:
With the development of Internet technology and electronic transactions, the problem of software security has become a reality that must be confronted and is no longer an option that can be abandoned. For this reason, software must be protected in all available ways. Where attackers use many methods to enable them to penetrate systems, especially those that rely on the Internet and hackers try to identify the vulnerabilities in the programs and exploit them to enter the database and steal sensitive information.
Electronic phishing is a form of illegal access to information, such as user names, passwords, credit card details, etc. Where attackers use different types of tricks to reveal confidential user information. Where attacks appear as links and phishing is done by clicking on the links contained in them. This leads to obtaining confidential information by using those false emails, redirecting the user without his knowledge to a site similar to the site he wants to access and capturing information. The main purpose of this paper is to protect users from malicious pages that are intended to steal personal information. Therefore, an electronic phishing detection algorithm called the SMO algorithm, which deals only with the properties of links, has been used.
Weka was used in the classification process. The samples were the characteristics of the links and they contain a number of sites which were 8266 and the number of phishing sites 4116 and legitimate sites 4150 sites and results were found to be new for the previous algorithms where the real classification rate 99.0202% in the time of 1.68 seconds.
11
Alizadehsani, Roohallah, Mohammad Javad Hosseini, Reihane Boghrati, Asma Ghandeharioun, Fahime Khozeimeh, and Zahra Alizadeh Sani. "Exerting Cost-Sensitive and Feature Creation Algorithms for Coronary Artery Disease Diagnosis." International Journal of Knowledge Discovery in Bioinformatics 3, no. 1 (January 2012): 59–79. http://dx.doi.org/10.4018/jkdb.2012010104.
Full textAbstract:
One of the main causes of death the world over is the family of cardiovascular diseases, of which coronary artery disease (CAD) is a major type. Angiography is the principal diagnostic modality for the stenosis of heart arteries; however, it leads to high complications and costs. The present study conducted data-mining algorithms on the Z-Alizadeh Sani dataset, so as to investigate rule based and feature based classifiers and their comparison, and the reason for the effectiveness of a preprocessing algorithm on a dataset. Misclassification of diseased patients has more side effects than that of healthy ones. To this end, this paper employs 10-fold cross-validation on cost-sensitive algorithms along with base classifiers of Naïve Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 and the results show that the SMO algorithm yielded very high sensitivity (97.22%) and accuracy (92.09%) rates.
12
Zhang, Yan Feng, and Ting Ting Li. "A Kind of Classification Algorithms of Data Mining and Quantitative Analysis." Advanced Materials Research 655-657 (January 2013): 963–68. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.963.
Full textAbstract:
C4.5, Bayesian network and Sequential Minimal Optimization (SMO) are three typical classification algorithms in data mining. Using cross-validation method with 10 folds get analysis and calculation results of the experiments for the three classification algorithms in the same training set and test set. The main metrics include accuracy, precision, speed, robustness, scalability and comprehensibility, we use margin curve show these. It provides a theoretical and experimental basis for users to select a proper classification algorithm with different training sets in quality and amount.
13
Gomathy, V., and Dr S. Sumathi. "IMPLEMENTATION OF SVM USING SEQUENTIAL MINIMAL OPTIMIZATION FOR POWER TRANSFORMER FAULT ANALYSIS USING DGA." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 5 (August 20, 2013): 1687–99. http://dx.doi.org/10.24297/ijct.v10i5.4153.
Full textAbstract:
Reliable operations of power transformers are necessary for effective transmission and distribution of power supply. During normal functions of the power transformer, distinct types of faults occurs due to insulation failure, oil aging products, overheating of windings, etc., affect the continuity of power supply thus leading to serious economic losses. To avoid interruptions in the power supply, various software fault diagnosis approaches are developed to detect faults in the power transformer and eliminate the impacts. SVM and SVM-SMO are the software fault diagnostic techniques developed in this paper for the continuous monitoring and analysis of faults in the power transformer. The SVM algorithm is faster, conceptually simple and easy to implement with better scaling properties for few training samples. The performances of SVM for large training samples are complex, subtle and difficult to implement. In order to obtain better fault diagnosis of large training data, SVM is optimized with SMO technique to achieve high interpretation accuracy in fault analysis of power transformer. The proposed methods use Dissolved Gas-in-oil Analysis (DGA) data set obtained from 500 KV main transformers of Pingguo Substation in South China Electric Power Company. DGA is an important tool for diagnosis and detection of incipient faults in the power transformers. The Gas Chromatograph (GC) is one of the traditional methods of DGA, utilized to choose the most appropriate gas signatures dissolved in transformer oil to detect types of faults in the transformer. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 2 GB RAM PC. The results obtained by optimized SVM and SVM-SMO are compared with the existing SVM classification techniques. The test results indicate that the SVM-SMO approach significantly improve the classification accuracy and computational time for power transformer fault classification.
14
Pentrakan, Amarawan, Cheng-Chia Yang, and Wing-Keung Wong. "How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?" International Journal of Environmental Research and Public Health 18, no. 11 (May 21, 2021): 5523. http://dx.doi.org/10.3390/ijerph18115523.
Full textAbstract:
The lack of an efficient approach in managing pharmaceutical prices in the procurement system led to a substantial burden on government budgets. In Thailand, although the reference price policy was implemented to contain the drug expenditure, there have been some challenges with the price dispersion of medicines and pricing information transparency. This phenomenon calls for the development of a potential algorithm to estimate appropriate prices for medical products. To serve this purpose, in this paper, we first developed the model by the sequential minimal optimization (SMO) algorithm for predicting the range of the prices for each medicine, using the Waikato environment for knowledge analysis software, and applying feature selection techniques also to examine improving predictive accuracy. We used the dataset comprised of 2424 records listed on the procurement system in Thailand from January to March 2019 in the application and used a 10-fold cross-validation test to validate the model. The results demonstrated that the model derived by the SMO algorithm with the gain ratio selection method provided good performance at an accuracy of approximately 92.62%, with high sensitivity and precision. Additionally, we found that the model can distinguish the differences in the prices of medicines in the pharmaceutical market by using eight major features—the segmented buyers, the generic product groups, trade product names, procurement methods, dosage forms, pack sizes, manufacturers, and total purchase budgets—that provided the highest predictive accuracy. Our findings are useful to health policymakers who could employ our proposed model in monitoring the situation of medicine prices and providing feedback directly to suggest the best possible price for hospital purchasing managers based on the feature inputs in their procurement system.
15
Nalluri, Madhu Sudana Rao, Kannan K, Xiao-Zhi Gao, Swaminathan V, and Diptendu Sinha Roy. "Parameter evolution of the classifiers for disease diagnosis with offline data-driven hybrid systems." Intelligent Data Analysis 24, no. 6 (December 18, 2020): 1365–84. http://dx.doi.org/10.3233/ida-194687.
Full textAbstract:
Automatic disease diagnosis is, in essence, a classification problem where the classifier has to be trained based on patients’ datasets and not entirely on doctors’ expert knowledge. In this paper, we present the design of such data-driven disease classifiers and fine-tuning classifier performance by a multi-objective evolutionary algorithm. We have used sequential minimal optimization (SMO) classifier as the base classifier and three evolutionary algorithms namely Cat Swarm Optimization (CSO), Invasive Weed Optimization (IWO) and Eagle Search based Invasive Weed Optimization (ESIWO) to diagnose disease from datasets available. In that sense, our approach is an offline data-driven approach with 18 benchmark medical datasets, and the obtained results demonstrate the superiority of the proposed diagnoses in terms of multiple objectives such as classification Prediction accuracy, Sensitivity, and Specificity. Relevant statistical tests have been carried out to substantiate the cogence of the obtained results.
16
Reddy, K. Ramakrishna, and Dr G. N. K. Suresh Babu. "A Novel Functional Machine Learning Approaches on Prostate Cancer." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1611–19. http://dx.doi.org/10.22214/ijraset.2022.41598.
Full textAbstract:
Abstract: Cancer registries are collections of curated data about malignant tumor diseases. The amount of data processed by cancer registries increases every year, making manual registration more and more tedious.This research work finds Bayes Net classifier gives an optimal results. The Sequential Minimal Optimization of functional machine learning approach is having highest accuracy level which is 85% of accuracy level. The Sequential Minimal Optimization of functional machine learning approach is having highest precision level which is 0.85 of precision level. The least precision value is 0.80 of precision value which is having Quadratic Discriminant Analysis of functional machine learning classifier approach. The Sequential Minimal Optimization of functional machine learning approach is having highest recall level which is 0.85 of recall level. The least recall value is 0.79 which is produced by Quadratic Discriminant Analysis functional machine learning classification approach. The Sequential Minimal Optimization of functional machine learning approach is having highest FMeasure level which is 0.85 of F-Measure level. The Fisher’s Discriminant Analysis algorithm of functional machine learning classifier and Linear Discriminant Analysis classification algorithm of functional machine learning classifier are having same receiver operating characteristic curve value which is 0.90 of receiver operating characteristic curve value.The maximum precision recall curve value is 0.90 of precision recall curve value which is produced by Linear Discriminant Analysis of functional machine learning classifier. This system recommends that the Sequential Minimal Optimization of functional machine learning approach produces optimal results compare with other models. Keywords: SMO, functional learning, LDA, QDA, and SDG
17
Lopes, Felipe F., João Canas Ferreira, and Marcelo A. C. Fernandes. "Parallel Implementation on FPGA of Support Vector Machines Using Stochastic Gradient Descent." Electronics 8, no. 6 (June 5, 2019): 631. http://dx.doi.org/10.3390/electronics8060631.
Full textAbstract:
Sequential Minimal Optimization (SMO) is the traditional training algorithm for Support Vector Machines (SVMs). However, SMO does not scale well with the size of the training set. For that reason, Stochastic Gradient Descent (SGD) algorithms, which have better scalability, are a better option for massive data mining applications. Furthermore, even with the use of SGD, training times can become extremely large depending on the data set. For this reason, accelerators such as Field-programmable Gate Arrays (FPGAs) are used. This work describes an implementation in hardware, using FPGA, of a fully parallel SVM using Stochastic Gradient Descent. The proposed FPGA implementation of an SVM with SGD presents speedups of more than 10,000× relative to software implementations running on a quad-core processor and up to 319× compared to state-of-the-art FPGA implementations while requiring fewer hardware resources. The results show that the proposed architecture is a viable solution for highly demanding problems such as those present in big data analysis.
18
Al-Ajeli, Ahmed, Raaid Alubady, and Eman S. Al-Shamery. "Improving spam email detection using hybrid feature selection and sequential minimal optimisation." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (July 1, 2020): 535. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp535-542.
Full textAbstract:
<p>Communication by email is counted as a popular manner through which users can exchange information. The email could be abused by spammers to spread suspicious content to the Internet users. Thus, the need to an effective way to detect spam emails are becoming clear to keep this information safe from malicious access. Many methods have been developed to address such a problem. In this paper, a machine learning technique is applied to detect spam emails. In this technique, a detection system based on sequential minimal optimization (SMO) is built to classify emails into two categories: spam and non-spam (ham). Each email is represented by a set of features extracted from its textual content. A hybrid feature selection is developed to choose a subset of these features based on their importance in process of the detection. This subset is then input into the SMO algorithm to make the detection decision. The use of such a technique provides an efficient protective mechanism to control spams. The experimental results show that the performance of the proposed method is promising compared with the existing methods.</p>
19
Al-Shargabi, Bassam, Fekry Olayah, and Waseem AL Romimah. "An Experimental Study for the Effect of Stop Words Elimination for Arabic Text Classification Algorithms." International Journal of Information Technology and Web Engineering 6, no. 2 (April 2011): 68–75. http://dx.doi.org/10.4018/jitwe.2011040106.
Full textAbstract:
In this paper, an experimental study was conducted on three techniques for Arabic text classification. These techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The paper assesses the accuracy for each classifier and determines which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for each classifier is measured by Percentage split method (holdout), and K-fold cross validation methods, along with the time needed to classify Arabic text. The results show that the SMO classifier achieves the highest accuracy and the lowest error rate, and shows that the time needed to build the SMO model is much lower compared to other classification techniques.
20
Mutlu, Gizen, and Çiğdem İnan Acı. "SVM-SMO-SGD: A hybrid-parallel support vector machine algorithm using sequential minimal optimization with stochastic gradient descent." Parallel Computing 113 (October 2022): 102955. http://dx.doi.org/10.1016/j.parco.2022.102955.
Full text
21
Khare, Neelu, Preethi Devan, Chiranji Chowdhary, Sweta Bhattacharya, Geeta Singh, Saurabh Singh, and Byungun Yoon. "SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection." Electronics 9, no. 4 (April 24, 2020): 692. http://dx.doi.org/10.3390/electronics9040692.
Full textAbstract:
The enormous growth in internet usage has led to the development of different malicious software posing serious threats to computer security. The various computational activities carried out over the network have huge chances to be tampered and manipulated and this necessitates the emergence of efficient intrusion detection systems. The network attacks are also dynamic in nature, something which increases the importance of developing appropriate models for classification and predictions. Machine learning (ML) and deep learning algorithms have been prevalent choices in the analysis of intrusion detection systems (IDS) datasets. The issues pertaining to quality and quality of data and the handling of high dimensional data is managed by the use of nature inspired algorithms. The present study uses a NSL-KDD and KDD Cup 99 dataset collected from the Kaggle repository. The dataset was cleansed using the min-max normalization technique and passed through the 1-N encoding method for achieving homogeneity. A spider monkey optimization (SMO) algorithm was used for dimensionality reduction and the reduced dataset was fed into a deep neural network (DNN). The SMO based DNN model generated classification results with 99.4% and 92% accuracy, 99.5%and 92.7% of precision, 99.5% and 92.8% of recall and 99.6%and 92.7% of F1-score, utilizing minimal training time. The model was further compared with principal component analysis (PCA)-based DNN and the classical DNN models, wherein the results justified the advantage of implementing the proposed model over other approaches.
22
Ma, Xin, Jiansheng Wu, and Xiaoyun Xue. "Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/524502.
Full textAbstract:
DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.
23
Z. Salih, Nibras, and Walaa Khalaf. "ON THE USE OF MULTIPLE INSTANCE LEARNING FOR DATA CLASSIFICATION." Journal of Engineering and Sustainable Development 25, Special (September 20, 2021): 1–127. http://dx.doi.org/10.31272/jeasd.conf.2.1.15.
Full textAbstract:
In the multiple instances learning framework, instances are arranged into bags, each bag contains several instances, the labels of each instance are not available but the label is available for each bag. Whilst in a single instance learning each instance is connected with the label that contains a single feature vector. This paper examines the distinction between these paradigms to see if it is appropriate, to cast the problem within a multiple instance framework. In single-instance learning, two datasets are applied (students’ dataset and iris dataset) using Naïve Bayes Classifier (NBC), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Sequential Minimal Optimization (SMO), while SimpleMI, MIWrapper, and MIBoost in multiple instances learning. Leave One Out Cross-Validation (LOOCV), five and ten folds Cross-Validation techniques (5-CV, 10-CV) are implemented to evaluate the classification results. A comparison of the result of these techniques is made, several algorithms are found to be more effective for classification in the multiple instances learning. The suitable algorithms for the students' dataset are MIBoost with MLP for LOOCV with an accuracy of 75%, whereas SimpleMI with SMO for the iris dataset is the suitable algorithm for 10-CV with an accuracy of 99.33%.
24
Moayedi, Bui, Kalantar, and Foong. "Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure." Applied Sciences 9, no. 21 (October 31, 2019): 4638. http://dx.doi.org/10.3390/app9214638.
Full textAbstract:
In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques.
25
Ghazali, Nurul Bashirah, Dang Fillatina Hashim, Fauziahanim Che Seman, Khalid Isa, Khairun Nidzam Ramli, Zuhairiah Zainal Abidin, Saizalmursidi Md Mustam, and Mohammed Al Haek. "Cable fault classification in ADSL copper access network using machine learning." International Journal of Advances in Intelligent Informatics 7, no. 3 (November 30, 2021): 318. http://dx.doi.org/10.26555/ijain.v7i3.488.
Full textAbstract:
Asymmetrical Digital Subscriber Line (ADSL) is the technology widely deployed worldwide, but its performance may be limited with respect to its intrinsic. The nature of the copper cable causes it to be more susceptible to signal degradation and faulty line. Common ADSL line faults are short-wired fault, open-wired fault, bridge taps, and uneven pair. However, ADSL technology is still one of the most established networks, and users in the suburban area still depend on the technology to access the internet service. This paper discussed and compared a machine learning algorithm based on Decision Trees (J48), K-Nearest Neighbor, Multi-level Perceptron, Naïve Bayes, Random Forest, and Sequential Minimal Optimization (SMO) for ADSL line impairment that affects the line operation performance concerning their percentage of accuracy. Resulting from classifications done using algorithms as mentioned above, the random forest algorithm gives the highest overall accuracy for the ADSL line impairment dataset. The best algorithm for classifying DSL line impairment is chosen based on the highest accuracy percentage. The accomplishment classification of fault type in the ADSL copper access network project may benefit the telecommunication network provider by remotely assessing the network condition rather than on-site.
26
Maputi, Edmund S., and Rajesh Arora. "Influence of geometric variables on spur gear volume." International Journal for Simulation and Multidisciplinary Design Optimization 11 (2020): 8. http://dx.doi.org/10.1051/smdo/2020003.
Full textAbstract:
Gear system optimization is currently topical amongst researchers. To this end, problem formulation is key and therefore knowledge of parameter influence and variation behaviour is indispensable. In this research work, four gear volume models were investigated for volume minimization while considering six variables viz. face width, module, pinion tooth, hardness, and pinion and gear shaft diameters. Three algorithms viz. teaching learning-based optimization (TLBO), particle swarm optimization (PSO) and firefly algorithm (FA) are employed to obtain the optimal volume and design parameter variation study. The convergence rate of each algorithm for each gear model is contrasted against other algorithms applied in the study. Experimental runs have also been conducted to determine standard deviation and mean values. Variation studies on the volume objective reflect relevant observations noted for parameter setting and optimization. The results obtained can assist the designer in setting designer preferences with minimal resources expended thereby improving the problem-solving exercise.
27
Kim, Kyungyeul, Han-Sung Kim, Jaekwoun Shim, and Ji Su Park. "A Study in the Early Prediction of ICT Literacy Ratings Using Sustainability in Data Mining Techniques." Sustainability 13, no. 4 (February 17, 2021): 2141. http://dx.doi.org/10.3390/su13042141.
Full textAbstract:
It would be very beneficial to determine in advance whether a student is likely to succeed or fail within a particular learning area, and it is hypothesized that this can be accomplished by examining student patterns based on the data generated before the learning process begins. Therefore, this article examines the sustainability of data-mining techniques used to predict learning outcomes. Data regarding students’ educational backgrounds and learning processes are analyzed by examining their learning patterns. When such achievement-level patterns are identified, teachers can provide the students with proactive feedback and guidance to help prevent failure. As a practical application, this study investigates students’ perceptions of computer and internet use and predicts their levels of information and communication technology literacy in advance via sustainability-in-data-mining techniques. The technique employed herein applies OneR, J48, bagging, random forest, multilayer perceptron, and sequential minimal optimization (SMO) algorithms. The highest early prediction result of approximately 69% accuracy was yielded for the SMO algorithm when using 47 attributes. Overall, via data-mining techniques, these results will aid the identification of students facing risks early on during the learning process, as well as the creation of customized learning and educational strategies for each of these students.
28
K. AL-Taie, Rana Riad, Basma Jumaa Saleh, Ahmed Yousif Falih Saedi, and Lamees Abdalhasan Salman. "Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 5229. http://dx.doi.org/10.11591/ijece.v11i6.pp5229-5239.
Full textAbstract:
Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods.
29
You, Shuang, and Yaping Zhou. "Optimization driven cellular automata for traffic flow prediction at signalized intersections." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 1547–66. http://dx.doi.org/10.3233/jifs-192099.
Full textAbstract:
The traffic flow prediction using cellular automata (CA) is a trendy research domain that identified the potential of CA in modelling the traffic flow. CA is a technique, which utilizes the basic units for describing the overall behaviour of complicated systems. The CA model poses a benefit for defining the characteristics of traffic flow. This paper proposes a modified CA model to reveal the prediction of traffic flows at the signalised intersection. Based on the CA model, the traffic density and the average speed are computed for studying the characteristics and spatial evolution of traffic flow in signalised intersection. Moreover, a CA model with a self-organizing traffic signal system is devised by proposing a new optimization model for controlling the traffic rules. The Sunflower Cat Optimization (SCO) algorithm is employed for efficiently predicting traffic. The SCO is designed by integrating the Sunflower optimization algorithm (SFO) and Cat swarm optimization (CSO) algorithm. Also, the fitness function is devised, which helps to guide the control rules evaluated by traffic simulation using the CA model. Thus, the cellular automaton is optimized using the SCO algorithm for predicting the traffic flows. The proposed Sunflower Cat Optimization-based cellular automata (SCO-CA) outperformed other methods with minimal travel time, distance, average traffic density, and maximal average speed.
30
Ropelewska, Ewa, Kadir Sabanci, and Muhammet Fatih Aslan. "The Changes in Bell Pepper Flesh as a Result of Lacto-Fermentation Evaluated Using Image Features and Machine Learning." Foods 11, no. 19 (September 21, 2022): 2956. http://dx.doi.org/10.3390/foods11192956.
Full textAbstract:
Food processing allows for maintaining the quality of perishable products and extending their shelf life. Nondestructive procedures combining image analysis and machine learning can be used to control the quality of processed foods. This study was aimed at developing an innovative approach to distinguishing fresh and lacto-fermented red bell pepper samples involving selected image textures and machine learning algorithms. Before processing, the pieces of fresh pepper and samples subjected to spontaneous lacto-fermentation were imaged using a digital camera. The texture parameters were extracted from images converted to different color channels L, a, b, R, G, B, X, Y, and Z. The textures after selection were used to build models for the classification of fresh and lacto-fermented samples using algorithms from the groups of Lazy, Functions, Trees, Bayes, Meta, and Rules. The highest average accuracy of classification reached 99% for the models developed based on sets of selected textures for color space Lab using the IBk (instance-based K-nearest learner) algorithm from the group of Lazy, color space RGB using SMO (sequential minimal optimization) from Functions, and color space XYZ and color channel X using IBk (Lazy) and SMO (Functions). The results confirmed the differences in image features of fresh and lacto-fermented red bell pepper and revealed the effectiveness of models built based on textures using machine learning algorithms for the evaluation of the changes in the pepper flesh structure caused by processing.
31
Baci, Nevila, Kreshnik Vukatana, and Marius Baci. "Machine Learning Approach for Intrusion Detection Systems as a Cyber Security Strategy for Small and Medium Enterprises." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 19 (January 20, 2022): 474–80. http://dx.doi.org/10.37394/23207.2022.19.43.
Full textAbstract:
Small and medium enterprises (SMEs) are businesses that account for a large percentage of the economy in many countries, but they lack cyber security. The present study examines different supervised machine learning methods with a focus on intrusion detection systems (IDSs) that will help in improving SMEs’ security. The algorithms that are tested through a real dataset, are Naïve Bayes, Sequential minimal optimization (SMO), C4.5 decision tree, and Random Forest. The experiments are run using the Waikato Environment for Knowledge Analyses (WEKA) 3.8.4 tools and the metrics used to evaluate the results were: accuracy, false-positive rate (FPR), and total time to train and build a classification model. The results obtained from the original dataset with 130 features show a high value of accuracy, but the computation time to build the classification model was notably high for the cases of C4.5 (1 hr. and 20 mins) and SMO algorithm (4 hrs. and 20 mins). the Information Gain (IG) method was used and the result was impressive. The time needed to train the model was reduced in the order of a few minutes and the accuracy was high (above 95%). In the end, challenges that SMEs can have for choosing an IDS such as lack of scalability and autonomic self-adaptation, can be solved by using a correct methodology with machine learning techniques.
32
Rghioui, Amine, Jaime Lloret, Sandra Sendra, and Abdelmajid Oumnad. "A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms." Healthcare 8, no. 3 (September 19, 2020): 348. http://dx.doi.org/10.3390/healthcare8030348.
Full textAbstract:
Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.
33
Bagriyanik, Selami, and Adem Karahoca. "Using Data Mining to Identify COSMIC Function Point Measurement Competence." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 5253. http://dx.doi.org/10.11591/ijece.v8i6.pp5253-5259.
Full textAbstract:
Cosmic Function Point (CFP) measurement errors leads budget, schedule and quality problems in software projects. Therefore, it’s important to identify and plan requirements engineers’ CFP training need quickly and correctly. The purpose of this paper is to identify software requirements engineers’ COSMIC Function Point measurement competence development need by using machine learning algorithms and requirements artifacts created by engineers. Used artifacts have been provided by a large service and technology company ecosystem in Telco. First, feature set has been extracted from the requirements model at hand. To do the data preparation for educational data mining, requirements and COSMIC Function Point (CFP) audit documents have been converted into CFP data set based on the designed feature set. This data set has been used to train and test the machine learning models by designing two different experiment settings to reach statistically significant results. Ten different machine learning algorithms have been used. Finally, algorithm performances have been compared with a baseline and each other to find the best performing models on this data set. In conclusion, REPTree, OneR, and Support Vector Machines (SVM) with Sequential Minimal Optimization (SMO) algorithms achieved top performance in forecasting requirements engineers’ CFP training need.
34
Yao, Weijia, Yongpeng Xu, Yong Qian, Gehao Sheng, and Xiuchen Jiang. "A Classification System for Insulation Defect Identification of Gas-Insulated Switchgear (GIS), Based on Voiceprint Recognition Technology." Applied Sciences 10, no. 11 (June 9, 2020): 3995. http://dx.doi.org/10.3390/app10113995.
Full textAbstract:
Insulation defects that occur in gas-insulated switchgear (GIS), which is one of the most important types of equipment in the power grid, can lead to serious accidents. The ultrasonic detection method is commonly used to detect partial discharge (PD) signals in power equipment to discover defects. However, the traditional method to diagnose defects in GIS with ultrasonic PD signals is still based on the experience of testers. In this study, a classification system was proposed to identify insulation defects of GIS, based on voiceprint recognition technology. Twelve coefficients from mel frequency cepstral coefficient (MFCC) and 24 delta MFCC features were extracted as the acoustic features of the system. A support vector machine (SVM) multi-classifier was constructed to perform the classification and the sequential minimal optimization (SMO) algorithm was used to optimize the computational efficiency of the SVM. The experiments were conducted on a 110 kV GIS with different kinds of insulation defects. The results verified that the classification system with SMO-SVM achieved better identification accuracy and efficiency than the system with SVM. Therefore, it reveals the feasibility of the system to realize identification of insulation defects in GIS automatically and accurately.
35
Al-Shamma’a, Abdullrahman A., Hammed O. Omotoso, Fahd A. Alturki, Hassan M. H. Farh, Abdulaziz Alkuhayli, Khalil Alsharabi, and Abdullah M. Noman. "Parameter Estimation of Photovoltaic Cell/Modules Using Bonobo Optimizer." Energies 15, no. 1 (December 26, 2021): 140. http://dx.doi.org/10.3390/en15010140.
Full textAbstract:
In this paper, a new application of Bonobo (BO) metaheuristic optimizer is presented for PV parameter extraction. Its processes depict a reproductive approach and the social conduct of Bonobos. The BO algorithm is employed to extract the parameters of both the single diode and double diode model. The good performance of the BO is experimentally investigated on three commercial PV modules (STM6-40 and STP6-120/36) and an R.T.C. France silicon solar cell under various operating circumstances. The algorithm is easy to implement with less computational time. BO is extensively compared to other state of the art algorithms, manta ray foraging optimization (MRFO), artificial bee colony (ABO), particle swarm optimization (PSO), flower pollination algorithm (FPA), and supply-demand-based optimization (SDO) algorithms. Throughout the 50 runs, the BO algorithm has the best performance in terms of minimal simulation time for the R.T.C. France silicon, STM6-40/36 and STP6-120/36 modules. The fitness results obtained through root mean square (RMSE), standard deviation (SD), and consistency of solution demonstrate the robustness of BO.
36
Ayhan, Tuğçe, and Tamer Uçar. "Determining customer limits by data mining methods in credit allocation process." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 1910. http://dx.doi.org/10.11591/ijece.v12i2.pp1910-1915.
Full textAbstract:
The demand for credit is increasing constantly. Banks are looking for various methods of credit evaluation that provide the most accurate results in a shorter period in order to minimize their rising risks. This study focuses on various methods that enable the banks to increase their asset quality without market loss regarding the credit allocation process. These methods enable the automatic evaluation of loan applications in line with the sector practices, and enable determination of credit policies/strategies based on actual needs. Within the scope of this study, the relationship between the predetermined attributes and the credit limit outputs are analyzed by using a sample data set of consumer loans. Random forest (RF), sequential minimal optimization (SMO), PART, decision table (DT), J48, multilayer perceptron(MP), JRip, naïve Bayes (NB), one rule (OneR) and zero rule (ZeroR) algorithms were used in this process. As a result of this analysis, SMO, PART and random forest algorithms are the top three approaches for determining customer credit limits.
37
Fang, HaiFeng, Jin Cao, LiHua Cai, Ta Zhou, and MingQiang Wang. "The recognition of plastic bottle using linear multi hierarchical SVM classifier." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11509–22. http://dx.doi.org/10.3233/jifs-202729.
Full textAbstract:
Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane could be recognized, improving the classification performance of SVM classifier in this layer. The loss function defined in this paper could control the depth of Linear Multi Hierarchical SVM (LMHSVM), the generalization parameters are added in the loss function and the input matrix to enhance the generalization performance of LMHSVM. The process of creating Bottle dataset by Histogram of Oriented Gradient (HOG) and Principal Component Analysis (PCA) is introduced meanwhile, reducing the data size of bottles. Experiments are conducted on LMHSVM and multiple typical classification algorithms with Bottle dataset and UCI datasets, the results indicated that LMHSVM has excellent classification performances than FNN classifier, LIBSVM (Gaussian) and GFS-AdaBoost-C in KEEL.
38
Saleh Hussein, Ameer, Rihab Salah Khairy, Shaima Miqdad Mohamed Najeeb, and Haider Th Salim Alrikabi. "Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 05 (March 16, 2021): 24. http://dx.doi.org/10.3991/ijim.v15i05.17173.
Full textAbstract:
<p>The global online communication channel made possible with the internet has increased credit card fraud leading to huge loss of monetary fund in their billions annually for consumers and financial institutions. The fraudsters constantly devise new strategy to perpetrate illegal transactions. As such, innovative detection systems in combating fraud are imperative to curb these losses. This paper presents the combination of multiple classifiers through stacking ensemble technique for credit card fraud detection. The fuzzy-rough nearest neighbor (FRNN) and sequential minimal optimization (SMO) are employed as base classifiers. Their combined prediction becomes data input for the meta-classifier, which is logistic regression (LR) resulting in a final predictive outcome for improved detection. Simulation results compared with seven other algorithms affirms that ensemble model can adequately detect credit card fraud with detection rates of 84.90% and 76.30%.</p>
39
Abd El Hamid, Marwa Mostafa, Mai S. Mabrouk, and Yasser M. K. Omar. "DEVELOPING AN EARLY PREDICTIVE SYSTEM FOR IDENTIFYING GENETIC BIOMARKERS ASSOCIATED TO ALZHEIMER’S DISEASE USING MACHINE LEARNING TECHNIQUES." Biomedical Engineering: Applications, Basis and Communications 31, no. 05 (September 9, 2019): 1950040. http://dx.doi.org/10.4015/s1016237219500406.
Full textAbstract:
Alzheimer’s disease (AD) is an irreversible, progressive disorder that assaults the nerve cells of the brain. It is the most widely recognized kind of dementia among older adults. Apolipoprotein E (APOE), is one of the most common genetic risk factors for AD whose significant association with AD is observed in various genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation among individuals. SNPs related to many common diseases like AD. SNPs are recognized as significant biomarkers for this disease, they help in understanding and detecting the disease in its early stages. Detecting SNPs biomarkers associated to the disease with high classification accuracy leads to early prediction and diagnosis. Machine learning techniques are utilized to discover new biomarkers of the disease. Sequential minimal optimization (SMO) algorithm with different kernels, Naive Bayes (NB), tree augmented Naive Bayes (TAN) and K2 learning algorithm have been applied on all genetic data of Alzheimer’s disease neuroimaging initiative phase 1 (ADNI-1)/Whole genome sequencing (WGS) datasets. The highest classification accuracy was achieved using 500 SNPs based on the [Formula: see text]-value threshold ([Formula: see text]-value [Formula: see text]). In whole genome approach ADNI-1, results revealed that NB and K2 learning algorithms scored an overall accuracy of 98% and 98.40%, respectively. In whole genome approach WGS, NB and K2 learning algorithms scored an overall accuracy of 99.63% and 99.75%, respectively.
40
Naseer, Mehwish, Wu Zhang, and Wenhao Zhu. "Prediction of Coding Intricacy in a Software Engineering Team through Machine Learning to Ensure Cooperative Learning and Sustainable Education." Sustainability 12, no. 21 (October 29, 2020): 8986. http://dx.doi.org/10.3390/su12218986.
Full textAbstract:
Coding deliverables are vital part of the software project. Teams are formed to develop a software project in a term. The performance of the team for each milestone results in the success or failure of the project. Coding intricacy is a major issue faced by students as coding is believed to be a complex field demanding skill and practice. Future education demands a smart environment for understanding students. Prediction of the coding intricacy level in teams can assist in cultivating a cooperative educational environment for sustainable education. This study proposed a boosting-based approach of a random forest (RF) algorithm of machine learning (ML) for predicting the coding intricacy level among software engineering teams. The performance of the proposed approach is compared with viable ML algorithms to evaluate its excellence. Results revealed promising results for the prediction of coding intricacy by boosting the RF algorithm as compared to bagging, J48, sequential minimal optimization (SMO), multilayer perceptron (MLP), and Naïve Bayes (NB). Logistic regression-based boosting (LogitBoost) and adaptive boosting (AdaBoost) are outperforming with 85.14% accuracy of prediction. The concerns leading towards high coding intricacy level can be resolved by discussing with peers and instructors. The proposed approach can ensure a responsible attitude among software engineering teams and drive towards fulfilling the goals of education for sustainable development by optimizing the learning environment.
41
Ghith, Ehab S., Mohamed Sallam, Islam S. M. Khalil, Mohamed Youssef Serry, and Sherif A. Hammad. "Design and Implementation of Tuning PID Controller using Modern Optimization Techniques for Micro-Robotics System." International Journal of Innovative Technology and Exploring Engineering 10, no. 11 (September 30, 2021): 51–68. http://dx.doi.org/10.35940/ijitee.j9454.09101121.
Full textAbstract:
One of the main difficult tasks in the field of micro-robotics is the process of the selection of the optimal parameters for the PID controllers. Some methods existed to solve this task and the common method used was the Ziegler and Nichols. The former method require an accurate mathematical model. This method is beneficial in linear systems, however, if the system becomes more complex or non-linear the method cannot produce accurate values to the parameters of the system. A solution proposed for this problem recently is the application of optimization techniques. There are various optimization techniques can be used to solve various optimization problems. In this paper, several optimization methods are applied to compute the optimal parameter of PID controllers. These methods are flower pollination algorithm (FPA), grey wolf optimization (GWO), sin cosine algorithm (SCA), slime mould algorithm (SMA), and sparrow search algorithm (SSA). The fitness function applied in the former optimization techniques is the integral square Time multiplied square Error (ISTES) as the performance index measure. The fitness function provides minimal rise time, minimal settling time, fast response, and no overshoot, Steady state error equal to zero, a very low transient response and a non-oscillating steady state response with excellent stabilization. The effectiveness of the proposed SSA-based controller was verified by comparisons made with FPA, GWO, SCA, SMA controllers in terms of time and frequency response. Each control technique will be applied to the identified model (simulation results) using MATLAB Simulink and the laboratory setup (experimental results) using LABVIEW software. Finally, the SSA showed the highest performance in time and frequency responses.
42
Doğdu, Cem, Thomas Kessler, Dana Schneider, Maha Shadaydeh, and Stefan R. Schweinberger. "A Comparison of Machine Learning Algorithms and Feature Sets for Automatic Vocal Emotion Recognition in Speech." Sensors 22, no. 19 (October 6, 2022): 7561. http://dx.doi.org/10.3390/s22197561.
Full textAbstract:
Vocal emotion recognition (VER) in natural speech, often referred to as speech emotion recognition (SER), remains challenging for both humans and computers. Applied fields including clinical diagnosis and intervention, social interaction research or Human Computer Interaction (HCI) increasingly benefit from efficient VER algorithms. Several feature sets were used with machine-learning (ML) algorithms for discrete emotion classification. However, there is no consensus for which low-level-descriptors and classifiers are optimal. Therefore, we aimed to compare the performance of machine-learning algorithms with several different feature sets. Concretely, seven ML algorithms were compared on the Berlin Database of Emotional Speech: Multilayer Perceptron Neural Network (MLP), J48 Decision Tree (DT), Support Vector Machine with Sequential Minimal Optimization (SMO), Random Forest (RF), k-Nearest Neighbor (KNN), Simple Logistic Regression (LOG) and Multinomial Logistic Regression (MLR) with 10-fold cross validation using four openSMILE feature sets (i.e., IS-09, emobase, GeMAPS and eGeMAPS). Results indicated that SMO, MLP and LOG show better performance (reaching to 87.85%, 84.00% and 83.74% accuracies, respectively) compared to RF, DT, MLR and KNN (with minimum 73.46%, 53.08%, 70.65% and 58.69% accuracies, respectively). Overall, the emobase feature set performed best. We discuss the implications of these findings for applications in diagnosis, intervention or HCI.
43
Ningrum, Ratih Ardiati, Indah Fahmiyah, Aretha Levi, and Muhammad Axel Syahputra. "Short birth intervals classification for Indonesia’s women." Bulletin of Electrical Engineering and Informatics 11, no. 3 (June 1, 2022): 1535–42. http://dx.doi.org/10.11591/eei.v11i3.3432.
Full textAbstract:
Birth interval is closely related to maternal and infant health. According to world health organization (WHO), the birth interval between two births is at least 33 months. This study is the first to discuss the short birth interval (SBI) in Indonesia and used data from the Indonesian Demographic and Health Surveys 2017 with a total of 34,200 respondents. Birth interval means the length of time between the birth of the first child and the second child. Categorized as SBI if the distance between births is less than 33 months. The variables used include mother's age, mother's age at first giving birth, father's age, household wealth, succeeding birth interval, breastfeeding status, child sex, residence, mother's education, health insurance, mother's working status, contraception used, child alive, total children, number of living children, and household members. Machine learning algorithms including logistic regression, Naïve Bayes, lazy locally weighted learning (LWL), and sequential minimal optimization (SMO) are applied to classify SBI. Based on the values of accuracy, precision, recall, F-score, matthews correlation coefficient (MCC), receiver operator characteristic (ROC) area, precision-recall curve (PRC) area, the Naïve Bayes is the best algorithm with scores obtained 0.891, 0.889, 0.891, 0.885, 0.687, 0.972, and 0.960 respectively. Additionally, 18.25% of mothers were classified as still giving birth within a short interval.
44
Chou, Jui-Sheng, Trang Thi Phuong Pham, and Chia-Chun Ho. "Metaheuristic Optimized Multi-Level Classification Learning System for Engineering Management." Applied Sciences 11, no. 12 (June 15, 2021): 5533. http://dx.doi.org/10.3390/app11125533.
Full textAbstract:
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.
45
Joshi, Ankur, Sukanya Sharma, N. V. M. Rao, and A. K. Vaish. "Usage of Machine Learning Algorithm Models to Predict Operational Efficiency Performance of Selected Banking Sectors of India." International Journal of Emerging Technology and Advanced Engineering 12, no. 6 (June 2, 2022): 105–14. http://dx.doi.org/10.46338/ijetae0622_14.
Full textAbstract:
—It was an attempt to predict the impact of NPAs in the selected public (SBI, BoI, BoB, BoM, CBoI, AB, CB, AlB,) and private (AxB, ICB, HDFCB and KB) banking sectors from 2008 to 2019. The data was also used to predict operational performance efficiency of these banking sectors after extracting through machine learning (ML) algorithm models and statistical interpretation of prediction accuracy by using WEKA tool. We used different models viz. NaiveBayes (NB), BayesNet (BN), logistic regression (LgR), Sequential minimal optimization of Support Vector Machine regression (SMOreg), Linear Logistic Regression (SL), Classification via Regression (CR), LogitBoost (LB); Logistic Model Tree (LMT), Random Forest & Random tree (RF & RT), Pruned & unpruned decision tree C4 (J48), and Class implementing minimal cost-complexity pruning (Cart) related to 15 attributes viz. GNPA, NNPA, GDP, CPI, PSL, TL, STA, GDP-1, RR, CPI-1, TE, TP and USTA as numeric as well as Banks, Year, GNPA>6, and GNPA>7, as nominal categories of dataset where overall performance accuracy was determined. The algorithm model classification predicted the highest values were for LB (78.47%) and Cart (74.30%) followed by J48 (73.61%), CR (72.91%) and LMT (69.44%) and lowest value in SMO (34.72%) as per 10-fold cross validation test. Additionally, these predicted results may have valuable implications for Indian banking sectors. We evaluated the operational efficiency as cumulative performance for 12 banking sectors as per assumed cut off values of GNPA. It may be varied with other independent variables like credit risk parameters, etc. It is suggested in future to study with parameters of deposit collection and investment to determine credit risk of these banking sectors. Keywords—Indian banking sectors, Machine learning models, Non-performing assets, Operational efficiency, WEKA tool
46
Reddy, Karna Vishnu Vardhana, Irraivan Elamvazuthi, Azrina Abd Aziz, Sivajothi Paramasivam, Hui Na Chua, and S. Pranavanand. "Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators." Applied Sciences 11, no. 18 (September 9, 2021): 8352. http://dx.doi.org/10.3390/app11188352.
Full textAbstract:
Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set.
47
Nayak, Suvra, Chhabi Panigrahi, Bibudhendu Pati, Sarmistha Nanda, and Meng-Yen Hsieh. "Comparative analysis of HAR datasets using classification algorithms." Computer Science and Information Systems, no. 00 (2021): 43. http://dx.doi.org/10.2298/csis201221043n.
Full textAbstract:
In the current research and development era, Human Activity Recognition (HAR) plays a vital role in analyzing the movements and activities of a human being. The main objective of HAR is to infer the current behaviour by extracting previous information. Now-a-days, the continuous improvement of living condition of human beings changes human society dramatically. To detect the activities of human beings, various devices, such as smartphones and smart watches, use different types of sensors, such as multi modal sensors, non-video based and video-based sensors, and so on. Among the entire machine learning approaches, tasks in different applications adopt extensively classification techniques, in terms of smart homes by active and assisted living, healthcare, security and surveillance, making decisions, tele-immersion, forecasting weather, official tasks, and prediction of risk analysis in society. In this paper, we perform three classification algorithms, Sequential Minimal Optimization (SMO), Random Forest (RF), and Simple Logistic (SL) with the two HAR datasets, UCI HAR and WISDM, downloaded from the UCI repository. The experiment described in this paper uses the WEKA tool to evaluate performance with the matrices, Kappa statistics, relative absolute error, mean absolute error, ROC Area, and PRC Area by 10-fold cross validation technique. We also provide a comparative analysis of the classification algorithms with the two determined datasets by calculating the accuracy with precision, recall, and F-measure metrics. In the experimental results, all the three algorithms with the UCI HAR datasets achieve nearly the same accuracy of 98%.The RF algorithm with the WISDM dataset has the accuracy of 90.69%,better than the others.
48
Rami, Abdelkader, Habib Hamdaoui, Houari Sayah, and Abdelkader Zeblah. "Efficient harmony search optimization for preventive-maintenance-planning for nuclear power systems." International Journal for Simulation and Multidisciplinary Design Optimization 5 (2014): A17. http://dx.doi.org/10.1051/smdo/2013011.
Full textAbstract:
This paper combines the universal generating function UGF with harmony search (HSO) meta-heuristic optimization method to solve a preventive maintenance (PM) problem for series-parallel system. In this work, we consider the situation where system and its components have several ranges of performance levels. Such systems are called multi-state systems (MSS). To enhance system availability or (reliability), possible schedule preventive maintenance actions are performed to equipments and affect strongly the effective age. The MSS measure is related to the ability of the system to satisfy the demand. The objective is to develop an algorithm to generate an optimal sequence of maintenance actions providing system working with the desired level of availability or (reliability) during its lifetime with minimal maintenance cost rate. To evaluate the MSS system availability, a fast method based on UGF is suggested. The harmony search approach can be applied as an optimization technique and adapted to this PM optimization problem.
49
Rawat, Romil, Yagya Nath Rimal, P. William, Snehil Dahima, Sonali Gupta, and K. Sakthidasan Sankaran. "Malware Threat Affecting Financial Organization Analysis Using Machine Learning Approach." International Journal of Information Technology and Web Engineering 17, no. 1 (January 1, 2022): 1–20. http://dx.doi.org/10.4018/ijitwe.304051.
Full textAbstract:
Since 2014, Emotet has been using Man-in-the-Browsers (MITB) attacks to target companies in the finance industry and their clients. Its key aim is to steal victims' online money-lending records and vital credentials as they go to their banks' websites. Without analyzing network packet payload computing (PPC), IP address labels, port number traces, or protocol knowledge, we have used Machine Learning (ML) modeling to detect Emotet malware infections and recognize Emotet related congestion flows in this work. To classify emotet associated flows and detect emotet infections, the output outcome values are compared by four separate popular ML algorithms: RF (Random Forest), MLP (Multi-Layer Perceptron), SMO (Sequential Minimal Optimization Technique), and the LRM (Logistic Regression Model). The suggested classifier is then improved by determining the right hyperparameter and attribute set range. Using network packet (computation) identifiers, the Random Forest classifier detects emotet-based flows with 99.9726 percent precision and a 92.3 percent true positive rating.
50
Oliveira Gonçalves, Carlos Adriano, Rui Camacho, Célia Talma Gonçalves, Adrián Seara Vieira, Lourdes Borrajo Diz, and Eva Lorenzo Iglesias. "Classification of Full Text Biomedical Documents: Sections Importance Assessment." Applied Sciences 11, no. 6 (March 17, 2021): 2674. http://dx.doi.org/10.3390/app11062674.
Full textAbstract:
The exponential growth of documents in the web makes it very hard for researchers to be aware of the relevant work being done within the scientific community. The task of efficiently retrieving information has therefore become an important research topic. The objective of this study is to test how the efficiency of the text classification changes if different weights are previously assigned to the sections that compose the documents. The proposal takes into account the place (section) where terms are located in the document, and each section has a weight that can be modified depending on the corpus. To carry out the study, an extended version of the OHSUMED corpus with full documents have been created. Through the use of WEKA, we compared the use of abstracts only with that of full texts, as well as the use of section weighing combinations to assess their significance in the scientific article classification process using the SMO (Sequential Minimal Optimization), the WEKA Support Vector Machine (SVM) algorithm implementation. The experimental results show that the proposed combinations of the preprocessing techniques and feature selection achieve promising results for the task of full text scientific document classification. We also have evidence to conclude that enriched datasets with text from certain sections achieve better results than using only titles and abstracts.