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Mohamad, Mumtazimah, Wan Nor Shuhadah Wan Nik, Zahrahtul Amani Zakaria, and Arifah Che Alhadi. "An Analysis of Large Data Classification using Ensemble Neural Network." International Journal of Engineering & Technology 7, no. 2.14 (April 6, 2018): 53. http://dx.doi.org/10.14419/ijet.v7i2.14.11155.
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In this paper, operational and complexity analysis are investigated for a proposed model of ensemble Artificial Neural Networks (ANN) multiple classifiers. The main idea to this is to employ more classifiers to obtain a more accurate prediction as well as to enhance the classification capabilities in case of larger data. The classification result analyzed between a single classifier and multiple classifiers followed by the estimates of upper bounds of converged functional error with the partitioning of the benchmark dataset. The estimates derived using the Apriori method shows that the proposed ensemble ANN algorithm with a different approach is feasible where such problems with a high number of inputs and classes can be solved with time complexity of O(n^k ) for some k, which is a type of polynomial. This result is in line with the significant performance achieved by the diversity rule applied with the use of reordering technique. As conclusion, an ensemble heterogeneous ANN classifier is practical and relevant to theoretical and experimental of combiners for the ensemble ANN classifier systems for a large dataset.
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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Potassium Channel Parameters With ANN And KNN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 926–33. http://dx.doi.org/10.18137/cardiometry.2022.25.926933.
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Aim: The Motive of this research is to analyze, compare ventricular Cardiac Arrhythmia (CA) classification using potassium channel (k+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) classifiers. Materials and Methods: D Noble Model For Human Ventricular Tissue (DNFHVT) is used for our classification. The DNFHVT is a mathematical model of action potential focusing on major ionic currents like K+,Na+ and Ca+.. Size of the sample was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20. These data are imported to KNN and ANN classifiers to find better accuracy among them. The accuracy of novel ANN and KNN classifiers for 20 samples is obtained by alternating the cross fold validation. These results will be imported to Statistical Package for the Social Science (SPSS) software to identify the overall accuracy for each classifier. Results: The results are obtained from SPSS for novel ANN and KNN classifiers. ANN shows accuracy of 13.14% with standard deviation (1.6800) and Standard error mean (0.3757). Similarly KNN produces an accuracy value of 7.19% with standard deviation (1.6902) and Standard error mean (0.377). Conclusion: As of the results, it clearly shows that ANN has better accuracy for classification than KNN.
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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Sodium Channel Parameters With ANN And KNN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 911–18. http://dx.doi.org/10.18137/cardiometry.2022.25.911918.
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Aim: Aim of this research is to analyze and compare ventricular Cardiac Arrhythmia (CA) classification using Sodium Channel (Na+) parameters with Artificial Neural Network (ANN) and K-Nearest Neighbour (KNN) classifiers. Materials and Methods: Ten Tusscher Human Ventricular Cell Model (THVCM) (data) is used for arrhythmias classification. THVCM has well defined sodium (Na+) channel dynamics. Sample size was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20 for each analysis and will be imported to the classifier, K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) classifier to find better accuracy. Finally, the results (accuracy) will be validated by using Statistical Package for the Social Science (SPSS) software. Result: Ventricular normal, tachycardia and bradycardia data are fed into novel ANN and KNN classifiers. The results obtained from classifiers for 20 samples are fed to SPSS. In that ANN shows accuracy of 35.6% with standard deviation (3.17822) and Standard error mean (0.71067). Similarly KNN produces an accuracy value of 18.05% with standard deviation (1.19593) and Standard error mean (0.26739). Conclusion: As per the results, it clearly shows that the novel ANN has better accuracy for classification than KNN.
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Benmouna, Brahim, Raziyeh Pourdarbani, Sajad Sabzi, Ruben Fernandez-Beltran, Ginés García-Mateos, and José Miguel Molina-Martínez. "Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves." Remote Sensing 14, no. 24 (December 16, 2022): 6366. http://dx.doi.org/10.3390/rs14246366.
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Tomato is an agricultural product of great economic importance because it is one of the most consumed vegetables in the world. The most crucial chemical element for the growth and development of tomato is nitrogen (N). However, incorrect nitrogen usage can alter the quality of tomato fruit, rendering it undesirable to customers. Therefore, the goal of the current study is to investigate the early detection of excess nitrogen application in the leaves of the Royal tomato variety using a non-destructive hyperspectral imaging system. Hyperspectral information in the leaf images at different wavelengths of 400–1100 nm was studied; they were taken from different treatments with normal nitrogen application (A), and at the first (B), second (C) and third (D) day after the application of excess nitrogen. We investigated the performance of nine machine learning classifiers, including two classic supervised classifiers, i.e., linear discriminant analysis (LDA) and support vector machines (SVMs), three hybrid artificial neural network classifiers, namely, hybrid artificial neural networks and independent component analysis (ANN-ICA), harmony search (ANN-HS) and bees algorithm (ANN-BA) and four classifiers based on deep learning algorithms by convolutional neural networks (CNNs). The results showed that the best classifier was a CNN method, with a correct classification rate (CCR) of 91.6%, compared with an average of 85.5%, 68.5%, 90.8%, 88.8% and 89.2% for LDA, SVM, ANN-ICA, ANN-HS and ANN-BA, respectively. This shows that modern CNN methods should be preferred for spectral analysis over other classical techniques. These CNN architectures can be used in remote sensing for the precise detection of the excessive use of nitrogen fertilizers in large extensions.
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Chang, Mahmud, Shin, Nguyen-Quang, Price, and Prithiviraj. "Comparison of Image Texture Based Supervised Learning Classifiers for Strawberry Powdery Mildew Detection." AgriEngineering 1, no. 3 (September 4, 2019): 434–52. http://dx.doi.org/10.3390/agriengineering1030032.
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Strawberry is an important fruit crop in Canada but powdery mildew (PM) results in about 30–70% yield loss. Detection of PM through an image texture-based system is beneficial, as it identifies the symptoms at an earlier stage and reduces labour intensive manual monitoring of crop fields. This paper presents an image texture-based disease detection algorithm using supervised classifiers. Three sites were selected to collect the leaf image data in Great Village, Nova Scotia, Canada. Images were taken under an artificial cloud condition with a Digital Single Lens Reflex (DSLR) camera as red-green-blue (RGB) raw data throughout 2017–2018 summer. Three supervised classifiers, including artificial neural networks (ANN), support vector machine (SVM), and k-nearest neighbors (kNN) were evaluated for disease detection. A total of 40 textural features were extracted using a colour co-occurrence matrix (CCM). The collected feature data were normalized, then used for training and internal, external and cross-validations of developed classifiers. Results of this study revealed that the highest overall classification accuracy was 93.81% using the ANN classifier and lowest overall accuracy was 78.80% using the kNN classifier. Results identified the ANN classifier disease detection having a lower Root Mean Square Error (RMSE) = 0.004 and Mean Absolute Error (MAE) = 0.003 values with 99.99% of accuracy during internal validation and 87.41%, 88.95% and 95.04% of accuracies during external validations with three different fields. Overall results demonstrated that an image texture-based ANN classifier was able to classify PM disease more accurately at early stages of disease development.
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Patgiri, Chayashree, and Amrita Ganguly. "Machine Learning Techniques for Automatic Detection of Sickle Cell Anemia using Adaptive Thresholding and Contour-based Segmentation Method." Asian Pacific Journal of Health Sciences 9, no. 4 (June 20, 2022): 165–70. http://dx.doi.org/10.21276/apjhs.2022.9.4.33.
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Automatic diagnosis of diseases in the medical field using image processing techniques has evolved tremendously in recent times. Sickle cell anemia (SCA) is a kind of disease connected with red blood cells (RBCs) present in the human body in which deformation of cells take place. The purpose of this work is to propose an automatic image processing technique for the detection of this disease from microscopic blood images. This paper mainly focuses on automatic detection of SCA using a novel segmentation method encompassing local adaptive thresholding and active contour-based algorithm. For the detection of sickle cells, supervised classifiers such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are used. Here, geometric features of healthy and unhealthy RBCs are calculated and applied to these classifiers. In this approach, performance is found slightly greater in SVM classifier than the ANN classifier trained with scaled conjugate gradient back-propagation (BP) algorithm and with hidden layer of ten neurons. The proposed approach achieves a maximum of 99.2% accuracy with SVM classifier. The performance is also studied for seven different training algorithms in the ANN classifier by varying the numbers of hidden layer neurons. Comparative analysis of the performances of these algorithms shows that, resilient BP algorithm and 10 numbers of hidden neurons gave moderately better performance in ANN with 99% accuracy. ANN and SVM classifier with adaptive thresholding and active contour technique is an efficient approach for the classification of patients with SCA.
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Manjunatha, G., and H. C. Chittappa. "Bearing Fault Classification Using Statistical Features And Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 4 (March 1, 2022): 104. http://dx.doi.org/10.18311/jmmf/2022/30676.
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<p>Bearing degradation is the most common source of faults in machines. In this context, this work presents a monitoring scheme to diagnose bearing faults using machine learning approach. In this approach classification of healthy and faulty conditions of the bearing is carried out using artificial neural network (ANN). A set of statistical features are extracted from the acquired vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features were classified using different classifiers. Based on the various classifier results obtained, the ANN classifier achieve the maximum classification accuracy which is recommended for online monitoring and fault diagnosis of the bearing in various machines.</p>
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Manjunatha, G., and H. C. Chittappa. "Bearing Fault Classification Using Statistical Features And Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 3A (July 12, 2022): 104. http://dx.doi.org/10.18311/jmmf/2022/30687.
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<p>Bearing degradation is the most common source of faults in machines. In this context, this work presents a monitoring scheme to diagnose bearing faults using machine learning approach. In this approach classification of healthy and faulty conditions of the bearing is carried out using artificial neural network (ANN). A set of statistical features are extracted from the acquired vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features were classified using different classifiers. Based on the various classifier results obtained, the ANN classifier achieve the maximum classification accuracy which is recommended for online monitoring and fault diagnosis of the bearing in various machines.</p>
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Masood, Ibrahim, Nadia Zulikha Zainal Abidin, Nur Rashida Roshidi, Noor Azlina Rejab, and Mohd Faizal Johari. "Design of an Artificial Neural Network Pattern Recognition Scheme Using Full Factorial Experiment." Applied Mechanics and Materials 465-466 (December 2013): 1149–54. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.1149.
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Automated recognition of process variation patterns using an artificial neural network (ANN) model classifier is a useful technique for multivariate quality control. Proper design of the classifier is critical for achieving effective recognition performance (RP). The existing classifiers were mainly designed empirically. In this research, full factorial design of experiment was utilized for investigating the effect of four design parameters, i.e., recognition window size, training data amount, training data quality and hidden neuron amount. The pattern recognition study focuses on bivariate correlated process mean shifts for cross correlation function, ρ = 0.1 ~ 0.9 and mean shifts, μ = ± 0.75 ~ 3.00 standard deviations. Raw data was used as input representation for a generalized model ANN classifier. The findings suggested that: (i) the best performance for each pattern could be achieved by setting different design parameters through specific classifiers, which (ii) gave superior result (average RP = 98.85%) compared to an empirical design (average RP = 96.5%). This research has provided a new perspective in designing ANN pattern recognition scheme in the field of statistical process control.
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Wang, Daliang, and Xiaowen Guo. "Research on Intelligent Recognition and Classification Algorithm of Music Emotion in Complex System of Music Performance." Complexity 2021 (June 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/4251827.
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In the complex system of music performance, there are differences in the expression of music emotions by listeners, so it is of great significance to study the classification of different emotions under different audio signals. In this paper, the research of human emotional intelligence recognition and classification algorithm in the complex system of music performance is proposed. Through the recognition of SVM, KNN, ANN, and ID3 classifiers, the accuracy of a single classifier is compared, and then the four classifiers are combined to compare the classification accuracy of audio signals before and after preprocessing. The results show that the accuracy of SVM and ANN fusion is the highest. Finally, recall and F1 are comprehensively compared in the fusion algorithm, and the fusion classification effect of SVM and ANN is better than that of the algorithm model.
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Aljammal, Ashraf H., Salah Taamneh, Ahmad Qawasmeh, and Hani Bani Salameh. "Machine Learning Based Phishing Attacks Detection Using Multiple Datasets." International Journal of Interactive Mobile Technologies (iJIM) 17, no. 05 (March 7, 2023): 71–83. http://dx.doi.org/10.3991/ijim.v17i05.37575.
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Nowadays, individuals and organizations are increasingly targeted by phishing attacks, so an accurate phishing detection system is required. Therefore, many phishing detection techniques have been proposed as well as phishing datasets have been collected. In this paper, three datasets have been used to train and test machine learning classifiers. The datasets have been archived by Phish-Tank and UCI Machine Learning Repository. Furthermore, Information Gain algorithm have been used for features reduction and selection purpose. In addition, six machine learning classifiers have been evaluated, namely NaiveBayes, ANN, DecisionStump, KNN, J48 and RandomForest. However, the classifiers have been trained and tested over the three datasets in two stages. The first stage is using all features included in each dataset while the second stage using selected features by IG algorithm. At the first stage RandomForest classifier has shown the best performance over Dataset-1 and Dataset-2, while J48 has shown the best performance over Dataset-3. On the other hand, after features selection, the RandomForest classifier was the superior among the other five classifiers over Dataset-1 and Dataset-2 with accuracy of 98% and 93.66% respectively. While ANN classifier has shown the best performance with accuracy of 88.92% over Dataset-3. Because of the few number of instances as well as features in Dataset-3 comparing to the other two dataset; the performance of the classifiers has been affected.
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Sabzi, Sajad, Razieh Pourdarbani, and Juan Ignacio Arribas. "A Computer Vision System for the Automatic Classification of Five Varieties of Tree Leaf Images." Computers 9, no. 1 (January 28, 2020): 6. http://dx.doi.org/10.3390/computers9010006.
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A computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid ANN–BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1–Cydonia oblonga (quince) 0.991 (ANN–ABC), 95.89% (ANN–ABC), 95.91% (ANN–ABC); 2–Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100% (LDA), 100% (LDA); 3–Malus pumila (apple) 0.996 (LDA), 96.63% (LDA), 94.99% (LDA); 4–Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71% (LDA), 82.57% (LDA); and 5–Prunus armeniaca (apricot) 0.994 (LDA), 88.67% (LDA), 94.65% (LDA), respectively.
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Khriji, Lazhar, Ahmed Chiheb Ammari, and Medhat Awadalla. "Hardware/Software Co-Design of a Vision System for Automatic Classification of Date Fruits." International Journal of Embedded and Real-Time Communication Systems 11, no. 4 (October 2020): 21–40. http://dx.doi.org/10.4018/ijertcs.2020100102.
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This paper proposes a hardware/software (HW/SW) co-design of an automatic classification system of Khalas, Khunaizi, Fardh, Qash, Naghal, and Maan dates fruit varieties in Oman. Three artificial intelligence (AI) techniques are used for qualitative comparisons: artificial neural network (ANN), support vector machine (SVM), and K-nearest neighbor (KNN). The accuracy performance of all AI classifiers is characterized for multiple color, shape, size, and texture feature combinations and for different critical parameter settings of the classifiers. In total, 600 date samples (100 dates/variety) are selected and imaged each sample individually. The system starts with preprocessing and segmentation of the colored input images. A total of 19 features are extracted from each image for use in classification models. The ANN classifier is shown to outperform all other classifiers. 97.26% highest classification accuracy is achieved using a combination of 15 color and shape-size features.
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Illias, Hazlee Azil, Ming Ming Lim, Ab Halim Abu Bakar, Hazlie Mokhlis, Sanuri Ishak, and Mohd Dzaki Mohd Amir. "Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence." PLOS ONE 16, no. 7 (July 1, 2021): e0253967. http://dx.doi.org/10.1371/journal.pone.0253967.
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In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.
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Manisha, Sanjeev Kr Dhull, and Krishna Kant Singh. "ECG Beat Classifiers: A Journey from ANN To DNN." Procedia Computer Science 167 (2020): 747–59. http://dx.doi.org/10.1016/j.procs.2020.03.340.
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Hichri, Amal, Mansour Hajji, Majdi Mansouri, Kamaleldin Abodayeh, Kais Bouzrara, Hazem Nounou, and Mohamed Nounou. "Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems." Sustainability 14, no. 17 (August 24, 2022): 10518. http://dx.doi.org/10.3390/su141710518.
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Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) systems is discussed in this work. Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. Only the most important features are selected to be supplied to the ANN classifier. The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. A thorough analysis of 16 faults applied on the module is performed. In general terms, the faults observed in the system are classified under three categories: simple, multiple, and mixed. The obtained results confirm the feasibility and effectiveness with a low computation time of the proposed approach for fault diagnosis.
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Suárez-Cuenca, Jorge Juan, Wei Guo, and Qiang Li. "INTEGRATION OF MULTIPLE CLASSIFIERS FOR COMPUTERIZED DETECTION OF LUNG NODULES IN CT." Biomedical Engineering: Applications, Basis and Communications 27, no. 04 (August 2015): 1550040. http://dx.doi.org/10.4015/s1016237215500404.
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The purpose of this study was to investigate the usefulness of various classifier combination methods for improving the performance of a computer-aided diagnosis (CAD) system for pulmonary nodule detection in computed tomography (CT). We employed 85 CT scans with 110 nodules in the publicly available Lung Image Database Consortium (LIDC) dataset. We first applied our CAD scheme trained previously to the LIDC cases for identifying initial nodule candidates, and extracting 18 features for each nodule candidate. We used eight individual classifiers for false positives (FPs) reduction, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Naïve Bayes, simple logistic, artificial neural network (ANN) and support vector machines (SVMs) with three different kernels. Five classifier combination methods were then employed to integrate the outputs of the eight individual classifiers for improving detection performance. The five combination methods included two supervised (a likelihood ratio (LR) method and a probability method based on the output scores of the eight individual classifiers) and three unsupervised ones (the sum, the product and the majority voting of the output scores from the eight individual classifiers). Leave-one-case-out approach was employed to train and test individual classifiers and supervised combination methods. At a sensitivity of 80%, the numbers of FPs per CT scan for the eight individual classifiers were 6.1 for LDA, 19.9 for QDA, 10.8 for Naïve Bayes, 8.4 for simple logistic, 8.6 for ANN, 23.7 for SVM-dot, 17.0 for SVM-poly, and 23.4 for SVM-anova; the numbers of FPs per CT scan for the five combination methods were 3.3 for the majority voting method, 5.0 for the sum, 4.6 for the product, 65.7 for the LR and 3.9 for the probability method. Compared to the best individual classifier, the majority voting method reduced 45% of FPs at 80% sensitivity. The performance of our CAD can be improved by combining multiple classifiers. The majority voting method achieved higher performance levels than other combination methods and all individual classifiers.
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Farhi, Lubna, Razia Zia, and Zain Anwar Ali. "Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images." Sir Syed University Research Journal of Engineering & Technology 8, no. 1 (December 19, 2018): 6. http://dx.doi.org/10.33317/ssurj.v8i1.36.
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Brain cancer has remained one of the key causes ofdeaths in people of all ages. One way to survival amongst patientsis to correctly diagnose cancer in its early stages. Recentlymachine learning has become a very important tool in medicalimage classification. Our approach is to examine and comparevarious machine learning classification algorithms that help inbrain tumor classification of Magnetic Resonance (MR) images.We have compared Artificial Neural Network (ANN), K-nearestNeighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine theaccuracy of each classifier and find the best amongst them forclassification of cancerous and noncancerous brain MR images.We have used 86 MR images and extracted a large number offeatures for each image. Since the equal number of images, havebeen used thus there is no suspicion of results being biased. Forour data set the most accurate results were provided by ANN. Itwas found that ANN provides better results for medium to largedatabase of Brain MR Images.
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Singh, Amritpal, and Sunil Kumar Chhillar. "News Category Classification Using Distinctive Bag of Words and ANN Classifier." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 311. http://dx.doi.org/10.23956/ijermt.v6i6.288.
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Category classification, for news, is a multi-label text classification problem. The goal is to assign one or more categories to a news article. A standard technique in multi-label text classification is to use a set of binary classifiers. For each category, a classifier is used to give a “yes” or “no” answer on if the category should be assigned to a text. Some of the standard algorithms for text classification that are used for binary classifiers include Naive Bayesian Classifiers, Support Vector Machines, artificial neural networks etc. In this distinctive bag of words have been used as feature set based on high frequency word tokens found in individual category of news. The algorithm presented in this work is based on a keyword extraction algorithm that is capable of dealing with English language in which different news categories i.e. Business, entertainment, politics, sports etc. has been considered. Intra-class news classification has been carried out in which Cricket and Football in sports category has been selected to verify the performance of the algorithm. Experimental results shows high classification rate in describing category of a news document.
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Kliangsuwan, Thitinan, and Apichat Heednacram. "Classifiers for Ground-Based Cloud Images Using Texture Features." Advanced Materials Research 931-932 (May 2014): 1392–96. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1392.
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The classification of ground-based cloud images has received more attention recently. The result of this work applies to the analysis of climate change; a correct classification is, therefore, important. In this paper, we used 18 texture features to distinguish 7 sky conditions. The important parameters of two classifiers are fine-tuned in the experiment, namely, k-nearest neighbor (k-NN) and artificial neural network (ANN). The performances of the two classifications were compared. Advantages and limitations of both classifiers were discussed. Our result revealed that the k-NN model performed at 72.99% accuracy while the ANN model has higher performance at 86.93% accuracy. We showed that our result is better than previous studies. Finally, seven most effective texture features are recommended to be used in the field of cloud type classification.
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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Calcium Channel Parameters With KNN And ANN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 919–25. http://dx.doi.org/10.18137/cardiometry.2022.25.919925.
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Aim: Aim of this research is to analyze and compare ventricular cardiac arrhythmia classification using calcium channel parameters with Artificial Neural Network (ANN) and K- Nearest Neighbour (KNN) classifier. Materials and Methods: For the classification of arrhythmias, A.V.Panifilov (AVP) is used. THVCM contains well defined Calcium channel dynamics and its properties. Sample size was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20 for each analysis and will be imported to the classifier such as K-Nearest Neighbour (KNN) and Artificial Neural Network (ANN) classifiers to find better accuracy. Finally, the results (accuracy) will be validated by using Statistical Package for the Social Science (SPSS) software. Results: The results obtained from Normal, Tachycardia and Bradycardia data are imported to the ANN and KNN classifier. In which KNN shows accuracy value (12.3950%), standard deviation (0.96490) and Standard error mean (0.21576). And ANN shows accuracy value (35.3400%), standard deviation (3.22285) and Standard error mean (0.72065). Conclusion: From the results, it is concluded that ANN produces better results when compared with KNN classification in terms of accuracy.
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Hond, Darryl, Hamid Asgari, Daniel Jeffery, and Mike Newman. "An Integrated Process for Verifying Deep Learning Classifiers Using Dataset Dissimilarity Measures." International Journal of Artificial Intelligence and Machine Learning 11, no. 2 (July 2021): 1–21. http://dx.doi.org/10.4018/ijaiml.289536.
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The specification and verification of algorithms is vital for safety-critical autonomous systems which incorporate deep learning elements. We propose an integrated process for verifying artificial neural network (ANN) classifiers. This process consists of an off-line verification and an on-line performance prediction phase. The process is intended to verify ANN classifier generalisation performance, and to this end makes use of dataset dissimilarity measures. We introduce a novel measure for quantifying the dissimilarity between the dataset used to train a classification algorithm, and the test dataset used to evaluate and verify classifier performance. A system-level requirement could specify the permitted form of the functional relationship between classifier performance and a dissimilarity measure; such a requirement could be verified by dynamic testing. Experimental results, obtained using publicly available datasets, suggest that the measures have relevance to real-world practice for both quantifying dataset dissimilarity, and specifying and verifying classifier performance.
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Sravanthi, D., and D. J. Rani. "Comparative Analysis of Hepatitis C Using Decision Tree Classifier and Artificial Neural Network Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 1017–23. http://dx.doi.org/10.18137/cardiometry.2022.25.10171023.
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Aim: The goal of this study is to compare the accuracy, sensitivity, and specificity of a Decision tree classifier and ANN classifier in detecting Innovative Hepatitis C Detection using modern methodologies. Materials and Methods: The data for this study was collected via the kaggle website. Samples were considered as (N=22) for Decision tree and (N=22) for ANN according to clinicalc.com, by keeping alpha error-threshold value 0.05, enrollment ratio as 0.1, 95% confidence interval,G power as 80%, total sample size calculated. The accuracy, sensitivity, and specificity were calculated using MATLAB and a standard data set. Results: Comparison of accuracy, sensitivity, and specificity is done by independent sample t-test SPSS software. There is a statistically insignificant difference between Decision Tree Classifier and Artificial Neural Network Classifiers. The Decision Tree with p=0.003, p<0.05-accuracy (0.41%), p=0.003, p<0.05-sensitivity (0.41%), p=0.570, p>0.05-specificity (0.42%) showed better results in comparison to ANN. Conclusion: Decision Tree showed better accuracy, sensitivity, and specificity than ANN to predict Innovative Hepatitis C Detection in a faster way.
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Deng, Guoqiang, Min Tang, Yuhao Zhang, Ying Huang, and Xuefeng Duan. "Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification." Applied Sciences 12, no. 24 (December 14, 2022): 12873. http://dx.doi.org/10.3390/app122412873.
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Artificial neural network (ANN) is powerful in the artificial intelligence field and has been successfully applied to interpret complex image data in the real world. Since the majority of images are commonly known as private with the information intended to be used by the owner, such as handwritten characters and face, the private constraints form a major obstacle in developing high-precision image classifiers which require access to a large amount of image data belonging to multiple users. State-of-the-art privacy-preserving ANN schemes often use full homomorphic encryption which result in a substantial overhead of computation and data traffic for the data owners, and are restricted to approximation models by low-degree polynomials which lead to a large accuracy loss of the trained model compared to the original ANN model in the plain domain. Consequently, it is still a huge challenge to train an ANN model in the encrypted-domain. To mitigate this problem, we propose a privacy-preserving ANN system for secure constructing image classifiers, named IPPNN, where the server is able to train an ANN-based classifier on the combined image data of all data owners without being able to observe any images using primitives, such as randomization and functional encryption. Our system achieves faster training time and supports lossless training. Moreover, IPPNN removes the need for multiple communications among data owners and servers. We analyze the security of the protocol and perform experiments on a large scale image recognition task. The results show that the IPPNN is feasible to use in practice while achieving high accuracy.
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Farhi, Lubna, Razia Zia, and Zain Anwar Ali. "Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images." Sir Syed University Research Journal of Engineering & Technology 8, no. 1 (December 19, 2018): 6. http://dx.doi.org/10.33317/ssurj.36.
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Brain cancer has remained one of the key causes of deaths in people of all ages. One way to survival amongst patients is to correctly diagnose cancer in its early stages. Recently machine learning has become a very important tool in medical image classification. Our approach is to examine and compare various machine learning classification algorithms that help in brain tumor classification of Magnetic Resonance (MR) images. We have compared Artificial Neural Network (ANN), K-nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine the accuracy of each classifier and find the best amongst them for classification of cancerous and noncancerous brain MR images. We have used 86 MR images and extracted a large number of features for each image. Since the equal number of images, have been used thus there is no suspicion of results being biased. For our data set the most accurate results were provided by ANN. It was found that ANN provides better results for medium to large database of Brain MR Images.
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Usama, Nayab, Imran Khan Niazi, Kim Dremstrup, and Mads Jochumsen. "Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network." Sensors 21, no. 18 (September 18, 2021): 6274. http://dx.doi.org/10.3390/s21186274.
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Error-related potentials (ErrPs) have been proposed as a means for improving brain–computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test–retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test–retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63–72% with LDA performing the best. There was no association between the individuals’ impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance.
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Murthy, H. S. Niranjana, and Dr M. Meenakshi. "ANN, SVM and KNN Classifiers for Prognosis of Cardiac Ischemia- A Comparison." Bonfring International Journal of Research in Communication Engineering 5, no. 2 (June 30, 2015): 07–11. http://dx.doi.org/10.9756/bijrce.8030.
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Farhi, Lubna, Razia Zia, and Zain Anwar Ali. "5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images." Sir Syed Research Journal of Engineering & Technology 1, no. 1 (December 19, 2018): 6. http://dx.doi.org/10.33317/ssurj.v1i1.36.
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Brain cancer has remained one of the key causes ofdeaths in people of all ages. One way to survival amongst patientsis to correctly diagnose cancer in its early stages. Recentlymachine learning has become a very important tool in medicalimage classification. Our approach is to examine and comparevarious machine learning classification algorithms that help inbrain tumor classification of Magnetic Resonance (MR) images.We have compared Artificial Neural Network (ANN), K-nearestNeighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine theaccuracy of each classifier and find the best amongst them forclassification of cancerous and noncancerous brain MR images.We have used 86 MR images and extracted a large number offeatures for each image. Since the equal number of images, havebeen used thus there is no suspicion of results being biased. Forour data set the most accurate results were provided by ANN. Itwas found that ANN provides better results for medium to largedatabase of Brain MR Images.
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Prasad, S. V. S., T. Satya Savithri, and Iyyanki V. Murali Krishna. "Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 3 (June 1, 2017): 1180. http://dx.doi.org/10.11591/ijece.v7i3.pp1180-1187.
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<p>The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multi-spectral image of Hyderabad and its surroundings area and also compare its performance with Artificial Neural Network (ANN) classifier. In this paper, a hybrid technique which we refer to as Fuzzy Incorporated Hierarchical clustering has been proposed for clustering the multispectral satellite images into LULC sectors. The experimental results show that overall accuracies of LULC classification of the Hyderabad and its surroundings area are approximately 93.159% for SVM and 89.925% for ANN. The corresponding kappa coefficient values are 0.893 and 0.843. The classified results show that the SVM yields a very promising performance than the ANN in LULC classification of high resolution Landsat-8 satellite images.</p>
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Kruglyak, Natan, and Robert Forchheimer. "Design of classifiers based on ANN approximations of traditional methods." International Journal of Circuit Theory and Applications 49, no. 7 (March 18, 2021): 1916–31. http://dx.doi.org/10.1002/cta.2998.
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Al-Zewairi, Malek, Sufyan Almajali, and Moussa Ayyash. "Unknown Security Attack Detection Using Shallow and Deep ANN Classifiers." Electronics 9, no. 12 (November 26, 2020): 2006. http://dx.doi.org/10.3390/electronics9122006.
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Advancements in machine learning and artificial intelligence have been widely utilised in the security domain, including but not limited to intrusion detection techniques. With the large training datasets of modern traffic, intelligent algorithms and powerful machine learning tools, security researchers have been able to greatly improve on the intrusion detection models and enhance their ability to detect malicious traffic more accurately. Nonetheless, the problem of detecting completely unknown security attacks is still an open area of research. The enormous number of newly developed attacks constitutes an eccentric challenge for all types of intrusion detection systems. Additionally, the lack of a standard definition of what constitutes an unknown security attack in the literature and the industry alike adds to the problem. In this paper, the researchers reviewed the studies on detecting unknown attacks over the past 10 years and found that they tended to use inconsistent definitions. This formulates the need for a standard consistent definition to have comparable results. The researchers proposed a new categorisation of two types of unknown attacks, namely Type-A, which represents a completely new category of unknown attacks, and Type-B, which represents unknown attacks within already known categories of attacks. The researchers conducted several experiments and evaluated modern intrusion detection systems based on shallow and deep artificial neural network models and their ability to detect Type-A and Type-B attacks using two well-known benchmark datasets for network intrusion detection. The research problem was studied as both a binary and multi-class classification problem. The results showed that the evaluated models had poor overall generalisation error measures, where the classification error rate in detecting several types of unknown attacks from 92 experiments was 50.09%, which highlights the need for new approaches and techniques to address this problem.
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Keyvanpour, Mohammad Reza, Mohammad Reza Ebrahimi, and Mostafa Javideh. "DESIGNING EFFICIENT ANN CLASSIFIERS FOR MATCHING BURGLARIES FROM DWELLING HOUSES." Applied Artificial Intelligence 26, no. 8 (September 2012): 787–807. http://dx.doi.org/10.1080/08839514.2012.718227.
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Sharma, Purva, Akash Saxena, and Qingsong Ai. "Critical investigations on performance of ANN and wavelet fault classifiers." Cogent Engineering 4, no. 1 (January 1, 2017): 1286730. http://dx.doi.org/10.1080/23311916.2017.1286730.
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Solanki, Yogendra Singh, Prasun Chakrabarti, Michal Jasinski, Zbigniew Leonowicz, Vadim Bolshev, Alexander Vinogradov, Elzbieta Jasinska, Radomir Gono, and Mohammad Nami. "A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches." Electronics 10, no. 6 (March 16, 2021): 699. http://dx.doi.org/10.3390/electronics10060699.
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Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier’s performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.
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Mahanya, G. B., and S. Nithyaselvakumari. "Analysis And Comparison Of Ventricular Cardiac Arrhythmia Classification Using Sodium Potassium Pump Channel Parameters With ANN And KNN Classifier." CARDIOMETRY, no. 25 (February 14, 2023): 934–41. http://dx.doi.org/10.18137/cardiometry.2022.25.934941.
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Aim: The intent of this research is to analyze and compare ventricular cardiac arrhythmia classification using sodium potassium pump (Na+/K+) channel parameters with Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) classifiers. Materials and Methods: P.J.Noble and A.V.Panfilov model (PJAV) is used for human ventricular study based on the action potential distance. PJAV uses alternative methods of computer simulations which include major ions, pumps and exchangers. Sample was calculated by keeping threshold 0.05, G Power 80%, confidence interval 95% and enrolment ratio as 1. Number of samples considered is 20. According to these data the accuracy is obtained from the classifiers by training novel ANN and KNN classifiers by alternating the Cross fold validation to obtain 20 different samples. These samples are imported to Statistical Package for the Social Science (SPSS) software for graphical representation and overall accuracy. Result: The concluded results shows that ANN has accuracy of 12.25% with standard deviation (4.0911) and Standard error mean (0.9148). Similarly KNN produces an accuracy value of 4.54 % with standard deviation (2.5732) and Standard error mean (0.5754). Conclusion: As of the results, it clearly shows that ANN has better accuracy for classification than KNN.
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Abdulameer, Ahmed Talib. "An Improvement of MRI Brain Images Classification Using Dragonfly Algorithm as Trainer of Artificial Neural Network." Ibn AL- Haitham Journal For Pure and Applied Science 31, no. 1 (May 10, 2018): 268. http://dx.doi.org/10.30526/31.1.1834.
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Computer software is frequently used for medical decision support systems in different areas. Magnetic Resonance Images (MRI) are widely used images for brain classification issue. This paper presents an improved method for brain classification of MRI images. The proposed method contains three phases, which are, feature extraction, dimensionality reduction, and an improved classification technique. In the first phase, the features of MRI images are obtained by discrete wavelet transform (DWT). In the second phase, the features of MRI images have been reduced, using principal component analysis (PCA). In the last (third) stage, an improved classifier is developed. In the proposed classifier, Dragonfly algorithm is used instead of backpropagation as training algorithm for artificial neural network (ANN). Some other recent training-based Neural Networks, SVM, and KNN classifiers are used for comparison with the proposed classifier. The classifiers are utilized to classify image as normal or abnormal MRI human brain image. The results show that the proposed classifier is outperformed the other competing classifiers.
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Tan, Maxine, Jiantao Pu, and Bin Zheng. "Optimization of Network Topology in Computer-Aided Detection Schemes Using Phased Searching with NEAT in a Time-Scaled Framework." Cancer Informatics 13s1 (January 2014): CIN.S13885. http://dx.doi.org/10.4137/cin.s13885.
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In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named “Phased Searching with NEAT in a Time-Scaled Framework” was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers – SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework – are performing comparably well in our mammographic mass detection scheme.
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Abdullah, Azian Azamimi, and Nur Siti Fatimah Azz-Zahra Md Som. "Design of an Intelligent Diagnostic System for Detection of Knee Injuries." Applied Mechanics and Materials 339 (July 2013): 219–24. http://dx.doi.org/10.4028/www.scientific.net/amm.339.219.
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Knee injuries is quite common in sport injuries and one of the most frequent happen are anterior cruciate ligament (ACL) knee injuries. There are two types of ACL injuries which are partial tear and complete tear. Currently physical tests, MRI image interpretation from expert and arthroscopy method are used to diagnoses the injuries. These procedures somehow are time consuming, invasive and operator dependent. To overcome these limitations, the intelligent diagnostic system using artificial neural network (ANN) has been proposed as an alternate way to give an early detection and also to classify the types of ACL injuries. We have used BP ANN and k-NN for the classification purpose. From these both classifiers, BP ANN give the higher accuracy which is 94.44% compared to k-NN classifier which the highest accuracy only up to 87.8333%.
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Moosavian, A., H. Ahmadi, A. Tabatabaeefar, and M. Khazaee. "Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing." Shock and Vibration 20, no. 2 (2013): 263–72. http://dx.doi.org/10.1155/2013/360236.
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Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.
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Rajendran, Priyanka, and Kirupa Ganapathy. "Neural network based seizure detection system using statistical package analysis." Bulletin of Electrical Engineering and Informatics 11, no. 5 (October 1, 2022): 2547–54. http://dx.doi.org/10.11591/eei.v11i5.3771.
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Due to the unpredictable interruptions within the functions of the human brain, disturbance occurs and it affects the behavior of the human and is equally laid low with the frequent occurrence termed as seizures. Therefore, the proposed system detects the seizure using machine learning algorithms. The electroencephalogram (EEG) contains information of the brain to detect the seizure. The objective is to evaluate the performance of machine learning classifiers K-nearest neighbors (KNN), artificial neural network (ANN), support vector machine (SVM) and principal component analysis (PCA) by comparing the accuracy of the classifier. This work uses total of 11,500 EEG samples from the UCI machine learning repository. The seizure detection was done in two ways. First method, features extracted from the EEG signal and classification techniques are done to classify the seizure. The second method uses the principal component analysis algorithm to improve the significant selections of features from the dataset. The outcomes are analyzed using the statistical package for the social science (SPSS) tools. ANN with extracted functions achieved 96% of accuracy and significant efficiency of (p less than 0.05) in comparison with different machine learning classifiers. It would be prudent to conclude that the ANN demonstrated the best accuracy, sensitivity, and specificity.
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Kaur, Sukhvinder, and J. S. Sohal. "Speech Activity Detection and its Evaluation in Speaker Diarization System." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 16, no. 1 (March 13, 2017): 7567–72. http://dx.doi.org/10.24297/ijct.v16i1.5893.
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In speaker diarization, the speech/voice activity detection is performed to separate speech, non-speech and silent frames. Zero crossing rate and root mean square value of frames of audio clips has been used to select training data for silent, speech and nonspeech models. The trained models are used by two classifiers, Gaussian mixture model (GMM) and Artificial neural network (ANN), to classify the speech and non-speech frames of audio clip. The results of ANN and GMM classifier are compared by Receiver operating characteristics (ROC) curve and Detection ErrorTradeoff (DET) graph. It is concluded that neural network based SADcomparatively better than Gaussian mixture model based SAD.
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Sabzi, Sajad, Razieh Pourdarbani, Mohammad Hossein Rohban, Alejandro Fuentes-Penna, José Luis Hernández-Hernández, and Mario Hernández-Hernández. "Classification of Cucumber Leaves Based on Nitrogen Content Using the Hyperspectral Imaging Technique and Majority Voting." Plants 10, no. 5 (April 29, 2021): 898. http://dx.doi.org/10.3390/plants10050898.
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Improper usage of nitrogen in cucumber cultivation causes nitrate accumulation in the fruit and results in food poisoning in humans; therefore, mandatory evaluation of food products becomes inevitable. Hyperspectral imaging has a very good ability to evaluate the quality of fruits and vegetables in a non-destructive manner. The goal of the present paper was to identify excess nitrogen in cucumber plants. To obtain a reliable result, the majority voting method was used, which takes into account the unanimity of five classifiers, namely, the hybrid artificial neural network–imperialism competitive algorithm (ANN-ICA), the hybrid artificial neural network–harmonic search (ANN-HS) algorithm, linear discrimination analysis (LDA), the radial basis function network (RBF), and the K-nearest-neighborhood (KNN). The wavelengths of 723, 781, and 901 nm were determined as optimal wavelengths using the hybrid artificial neural network–biogeography-based optimization (ANN-BBO) algorithm, and the performance of classifiers was investigated using the optimal spectrum. The results of a t-test showed that there was no significant difference in the precision of the algorithm when using the optimal wavelengths and wavelengths of the whole range. The correct classification rate of the classifiers ANN-ICA, ANN-HS, LDA, RBF, and KNN were 96.14%, 96.11%, 95.73%, 64.03%, and 95.24%, respectively. The correct classification rate of majority voting (MV) was 95.55% for test data in 200 iterations, which indicates the system was successful in distinguishing nitrogen-rich leaves from leaves with a standard content of nitrogen.
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Alfatni, Meftah Salem M., Siti Khairunniza-Bejo, Mohammad Hamiruce B. Marhaban, Osama M. Ben Saaed, Aouache Mustapha, and Abdul Rashid Mohamed Shariff. "Towards a Real-Time Oil Palm Fruit Maturity System using Supervised Classifiers Based on Feature Analysis." Agriculture 12, no. 9 (September 14, 2022): 1461. http://dx.doi.org/10.3390/agriculture12091461.
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Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by general and standard steps, such as image acquisition, pre-processing and segmentation, extraction and classification of image characteristics. This paper describes the design and implementation of a real-time fresh fruit bunch (FFB) maturity classification system for palm oil based on unrestricted remote sensing (CCD camera sensor) and image processing techniques using five multivariate techniques (statistics, histograms, Gabor wavelets, GLCM and BGLAM) to extract fruit image characteristics and incorporate information on palm oil species classification FFB and maturity testing. To optimize the proposed solution in terms of performance reporting and processing time, supervised classifiers, such as support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN), were performed and evaluated via ROC and AUC measurements. The experimental results showed that the FFB classification system of non-destructive palm oil maturation in real time provided a significant result. Although the SVM classifier is generally a robust classifier, ANN has better performance due to the natural noise of the data. The highest precision was obtained on the basis of the ANN and BGLAM algorithms applied to the texture of the fruit. In particular, the robust image processing algorithm based on BGLAM feature extraction technology and the ANN classifier largely provided a high AUC test accuracy of over 93% and an image-processing time of 0,44 (s) for the detection of FFB palm oil species.
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Imanian, Kamal, Razieh Pourdarbani, Sajad Sabzi, Ginés García-Mateos, Juan Ignacio Arribas, and José Miguel Molina-Martínez. "Identification of Internal Defects in Potato Using Spectroscopy and Computational Intelligence Based on Majority Voting Techniques." Foods 10, no. 5 (April 30, 2021): 982. http://dx.doi.org/10.3390/foods10050982.
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Potatoes are one of the most demanded products due to their richness in nutrients. However, the lack of attention to external and, especially, internal defects greatly reduces its marketability and makes it prone to a variety of diseases. The present study aims to identify healthy-looking potatoes but with internal defects. A visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectrometer was used to capture spectral data from the samples. Using a hybrid of artificial neural networks (ANN) and the cultural algorithm (CA), the wavelengths of 861, 883, and 998 nm in Vis/NIR region, and 1539, 1858, and 1896 nm in the SWIR region were selected as optimal. Then, the samples were classified into either healthy or defective class using an ensemble method consisting of four classifiers, namely hybrid ANN and imperialist competitive algorithm (ANN-ICA), hybrid ANN and harmony search algorithm (ANN-HS), linear discriminant analysis (LDA), and k-nearest neighbors (KNN), combined with the majority voting (MV) rule. The performance of the classifier was assessed using only the selected wavelengths and using all the spectral data. The total correct classification rates using all the spectral data were 96.3% and 86.1% in SWIR and Vis/NIR ranges, respectively, and using the optimal wavelengths 94.1% and 83.4% in SWIR and Vis/NIR, respectively. The statistical tests revealed that there are no significant differences between these datasets. Interestingly, the best results were obtained using only LDA, achieving 97.7% accuracy for the selected wavelengths in the SWIR spectral range.
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Chun, Teo Hong, Ummi Raba'ah Hashim, Sabrina Ahmad, Lizawati Salahuddin, Ngo Hea Choon, Kasturi Kanchymalay, and Nur Haslinda Ismail. "Identification of wood defect using pattern recognition technique." International Journal of Advances in Intelligent Informatics 7, no. 2 (April 19, 2021): 163. http://dx.doi.org/10.26555/ijain.v7i2.588.
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This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the F-measure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance.
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Barkana, Buket D., Burak Uzkent, and Inci Saricicek. "Normal and Abnormal Non-Speech Audio Event Detection Using MFCC and PR-Based Feature Sets." Advanced Materials Research 601 (December 2012): 200–208. http://dx.doi.org/10.4028/www.scientific.net/amr.601.200.
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Non-speech audio event detection and classification has become a very active subject of research, since it can be implemented in many important areas: audio surveillance and context awareness systems. In this study, non-speech normal and abnormal audio events were detected by Mel-frequency cepstrum coefficients (MFCC) and Pitch range (PR) based features using artificial neural network (ANN) classifiers. We have 4 abnormal events (glass breaking, dog barking, scream, gunshot) and 2 normal events (engine noise and rain). Event detection, using ANN classifiers, resulted in an accuracy of up to 92%, with recognition rates overall in the range of 78%-87.5%.
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Mas-Cabo, J., G. Prats-Boluda, J. Garcia-Casado, J. Alberola-Rubio, A. Perales, and Y. Ye-Lin. "Design and Assessment of a Robust and Generalizable ANN-Based Classifier for the Prediction of Premature Birth by means of Multichannel Electrohysterographic Records." Journal of Sensors 2019 (November 25, 2019): 1–13. http://dx.doi.org/10.1155/2019/5373810.
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Preterm labor is one of the major causes of neonatal deaths and also the cause of significant health and development impairments in those who survive. However, there are still no reliable and accurate tools for preterm labor prediction in clinical settings. Electrohysterography (EHG) has been proven to provide relevant information on the labor time horizon. Many studies focused on predicting preterm labor by using temporal, spectral, and nonlinear parameters extracted from single EHG recordings. However, multichannel analysis, which includes information from the whole uterus and about coupling between the recording areas, may provide better results. The cross validation method is often used to design classifiers and evaluate their performance. However, when the validation dataset is used to tune the classifier hyperparameters, the performance metrics of this dataset may not properly assess its generalization capacity. In this work, we developed and compared different classifiers, based on artificial neural networks, for predicting preterm labor using EHG features from single and multichannel recordings. A set of temporal, spectral, nonlinear, and synchronization parameters computed from EHG recordings was used as the input features. All the classifiers were evaluated on independent test datasets, which were never “seen” by the models, to determine their generalization capacity. Classifiers’ performance was also evaluated when obstetrical data were included. The experimental results show that the classifier performance metrics were significantly lower in the test dataset (AUC range 76-91%) than in the train and validation sets (AUC range 90-99%). The multichannel classifiers outperformed the single-channel classifiers, especially when information was combined into mean efficiency indexes and included coupling information between channels. Including obstetrical data slightly improved the classifier metrics and reached an AUC of 91.1±2.5% for the test dataset. These results show promise for the transfer of the EHG technique to preterm labor prediction in clinical practice.
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Perfecto-Avalos, Yocanxóchitl, Luis Villela, Alejandro Garcia-Gonzalez, Ana G. Hernández-Reynoso, Gildardo Sánchez-Ante, Rita Q. Fuentes-Aguilar, Sean Scott, Carlos Ortiz-Hidalgo, and Jose Borbolla Escoboza. "Molecular Subtype Classification of Diffuse Large B-Cell Lymphoma By Immunohistochemical Algorithms and Automatic Supervised Classifiers." Blood 128, no. 22 (December 2, 2016): 4229. http://dx.doi.org/10.1182/blood.v128.22.4229.4229.
Full textAbstract:
Abstract Diffuse large B-cell lymphoma (DLBCL) can be classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) molecular subtype. These entities are driven by different intracellular oncogenic signaling pathways that lead to a distinct clinical outcome (Fang, Xu, & Li, 2010; Lenz et al., 2008). Several immunohistochemical (IHC)-based DLBCL classification algorithms have been proposed, this considers the case when genetic expression profile (GEP) studies are not available. However, there is a major discrepancy within IHC algorithms, and when they are compared to GEP (Coutinho et al., 2013). To address these inconsistencies and determine if an automatic classifier could be used to accurately categorize DLBCL subtype, we perfomed a present a performance comparison between eight reported IHC algorithms (Colomo, Hans, Hans modified [Hans*], Nyman, Choi, Choi modified [Choi*] and Visco-Young with three [VY3] and four [VY4] antibodies) against their counterparts developed by automatic classification techniques, which consider the following structures: Bayesian Classifier (B), Bayesian Simple Classifier (BS), Naïve Bayesian Classifier (BN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM). The Visco-Young database (Visco et al., 2012), which contains GEP, IHC raw data corresponding to GCET1, MUM1, FOXP1, BCL6, and CD10 antibodies, and clinical information of 475 de novo DLBCL patients, was used. According to GEP, the database contained 231 GCB, and 244 non-GCB cases. Each patient in VY database was ranked by survival rate as: low survival (0 - 34 months, 237 patients), medium survival (35 - 69 months, 173 patients), or high survival (70 - 106 months, 65 patients) rate. For the implementation of automatic classifiers, the database was split into training, testing and validation data subsets (75%, 20% and 5% respectively) by random selection, but to preserve the same proportion of ranked patients, the so-called k-fold cross-validation technique was applied. The automatic classifier versions of IHC algorithms used the same raw IHC data (antibody combination) as the input, e.g. VY3 used CD10, FOXP1, and BCL6 raw IHC as well as the ANN VY3. A total of 35 automatic classifiers were trained, where Colomo and Hans use the same set of antibodies and are represented by the same automatic classifiers. The stopping criterion during the training stage for all classification algorithms was an error less than 1x10-3 or 100 training epochs, whichever was satisfied first. The performance of the eight IHC algorithms and the automatic classifiers was evaluated by computing the accuracy (Acc), specificity (Spec), and sensitivity (Sens), according to the Receiver Operating Characteristic procedure. Five classifiers obtained the highest metrics: ANN Choi, BS Choi, and BS Choi* with 94.2% Acc, 93.1% Spec, and 95.2% Sens, followed by SVM Choi and SVM VY4 with 94.2% Acc, 91.4% Spec, and 96.8% Sens. Choi was the IHC algorithm with better metrics (92.5% Acc, 84.5% Spec, and 100% Sens), which ranked 11 out of 43 models tested, followed by VY3 and VY4 (ranked 22 and 23, respectively). Survival of GCB and non-GCB groups identified by these models were compared using Kaplan-Meier curves, and the significance was calculated using log-rank test. For the best five automatic classifiers and the Choi IHC algorithm, GCB overall survival was better than non-GCB cases (p < 0.05). To statistically compare the models with GEP, all automatic classifiers and IHC algorithms results were analyzed by Cohen's kappa (κ) for agreement analysis and Pearson's chi-squared test. Only Choi IHC algorithm had a very good agreement when compared with GEP (κ = 0.85, p < 0.001). The best five automatic supervised classifiers provided a perfect agreement with GEP (κ = 0.88, p < 0.001). Moreover, the agreement between IHC algorithms was mainly from moderate to good (κ: 0.41 - 0.79), except for Choi having a very good agreement with both VY3 and VY4 (κ = 0.95, p < 0.001). Conversely, a very good agreement within supervised classifiers was observed (κ: 0.77 - 1.00). Harnessing all of the available immunohistochemical data in order to increase the DLBCL classification accuracy when compared with decision three pre-existing algorithms, we conclude that 4 antibody-based BS Choi* automatic classifier provided the best metrics and represents an affordable and time-saving alternative for DLBCL molecular subtype identification. Disclosures No relevant conflicts of interest to declare.
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Ang, Rebecca P., and Dion H. Goh. "Predicting Juvenile Offending." International Journal of Offender Therapy and Comparative Criminology 57, no. 2 (December 12, 2011): 191–207. http://dx.doi.org/10.1177/0306624x11431132.
Full textAbstract:
In this study, the authors compared logistic regression and predictive data mining techniques such as decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs), and examined these methods on whether they could discriminate between adolescents who were charged or not charged for initial juvenile offending in a large Asian sample. Results were validated and tested in independent samples with logistic regression and DT, ANN, and SVM classifiers achieving accuracy rates of 95% and above. Findings from receiver operating characteristic analyses also supported these results. In addition, the authors examined distinct patterns of occurrences within and across classifiers. Proactive aggression and teacher-rated conflict consistently emerged as risk factors across validation and testing data sets of DT and ANN classifiers, and logistic regression. Reactive aggression, narcissistic exploitativeness, being male, and coming from a nonintact family were risk factors that emerged in one or more of these data sets across classifiers, while anxiety and poor peer relationships failed to emerge as predictors.
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Yuvaraj, N., K. Srihari, Gaurav Dhiman, K. Somasundaram, Ashutosh Sharma, S. Rajeskannan, Mukesh Soni, Gurjot Singh Gaba, Mohammed A. AlZain, and Mehedi Masud. "Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking." Mathematical Problems in Engineering 2021 (February 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/6644652.
Full textAbstract:
In the modern era, the cyberbullying (CB) is an intentional and aggressive action of an individual or a group against a victim via electronic media. The consequence of CB is increasing alarmingly, affecting the victim either physically or psychologically. This allows the use of automated detection tools, but research on such automated tools is limited due to poor datasets or elimination of wide features during the CB detection. In this paper, an integrated model is proposed that combines both the feature extraction engine and classification engine from the input raw text datasets from a social media engine. The feature extraction engine extracts the psychological features, user comments, and the context into consideration for CB detection. The classification engine using artificial neural network (ANN) classifies the results, and it is provided with an evaluation system that either rewards or penalizes the classified output. The evaluation is carried out using Deep Reinforcement Learning (DRL) that improves the performance of classification. The simulation is carried out to validate the efficacy of the ANN-DRL model against various metrics that include accuracy, precision, recall, and f-measure. The results of the simulation show that the ANN-DRL has higher classification results than conventional machine learning classifiers.