Sensor signals have been frequently used for activity recognition and gender classification in the literature. In this study, a new multikernel local diamond pattern (MK-LDP) is proposed as a novel ...local descriptor method for feature extraction. The proposed MK-LDP is aimed to generate distinctive features from a signal or image using vertical and horizontal diamond-like patterns and multikernel functions. These kernels are signum, ternary, and quaternary. MK-LDP is utilized as a feature extraction technique for human activity recognition (HAR). The proposed HAR method has four fundamental phases, namely, preprocessing, feature generation with MK-LDP, informative features selection using hybrid ReliefF and neighborhood component analysis (RFNCA), and classification using support vector machine (SVM). The proposed MK-LDP extracts 2560 features from the raw sensor signals, RFINCA selects 512 most meaningful ones from the extracted 2560 features, and then the selected 512 features are used as input of the SVM for HAR. Three cases are defined to test the proposed MK-LDP- and RFNCA-based HAR method. These cases are gender classification, activity classification, and both gender and activity classification, respectively. The achieved best accuracy rates are 99.47%, 99.71%, and 99.36% for gender, daily sports activities, and both gender and daily sports activities recognition, respectively. The proposed MK-LDP-based method is also compared to the state-of-the-art and deep learning techniques. The obtained results revealed that the proposed MK-LDP- and RFNCA-based framework is successful HAR using sensor signals.
Meta-heuristic optimization algorithms have been used to solve mathematically unidentifiable problems. The main purpose of the optimization methods on problem-solving is to choose the best solution ...in predefined conditions. To increase performance of the optimization methods, chaotic maps for instance Logistic, Singer, Sine, Tent, Chebyshev, Circle have been widely used in the literature. However, hybrid 1D chaotic maps have higher performance than the 1D chaotic maps. The hybrid chaotic maps have not been used in the optimization process. In this article, 1D hybrid chaotic map (logistic-sine map)-based novel swarm optimization method is proposed to achieve higher numerical results than other optimization methods. Logistic-sine map has good statistical result, and this advantage is used directly to calculate global optimum value in this study. The proposed algorithm is a swarm-based optimization algorithm, and the seed value of the logistic-sine map is generated from local best solutions to reach global optimum. In order to test the proposed hybrid chaotic map-based optimization method, widely used numerical benchmark functions are chosen. The proposed chaotic optimization method is also tested on compression spring design problem. Results and comparisons clearly show that the proposed chaotic optimization method is successful.
•Automated prosthetic hands control framework using (sEMG) is proposed.•A novel ternary pattern and discrete wavelet (TP-DWT) based iterative feature extraction method is proposed.•The proposed ...TP-DWT based sEMG classification method achieved 99.14 % accuracy.
Hands are two of the most crucial organs and they play major role for human activities. Therefore, amputee people experience many difficulties in daily life. To overcome these difficulties, prosthetic hand is an effective solution. In order to automate the control of prosthetic hands, surface electromyogram (sEMG) signals and machine learning techniques play vital role. In this work, a novel ternary pattern and discrete wavelet (TP-DWT) based iterative feature extraction method is proposed. By using the proposed TP-DWT based feature extraction network, a sEMG signal recognition method is presented. The proposed TP-DWT based sEMG signal recognition method consists of channel concatenation, feature extraction using TP-DWT network, feature selection by using 2 leveled feature selection method and classification using conventional classifiers. The proposed method is tested by using a sEMG dataset, which were collected from amputee participants with 3 force levels (Low, Moderate, High). Four cases were studied to comprehensively evaluate the proposed TP-DWT based hand movements classification method with the sEMG signals. Based on the evaluations, the proposed TP-DWT based sEMG classification method achieved 99.14 % accuracy rate for all force levels by using k-nearest neighbor (k-NN) classifier with 10-fold cross validation. Moreover 97.78 %, 93.33 % and 92.96 % success rates are achieved for Low, Moderate and High force levels respectively.
Introduction
The differential diagnosis of anemia is an important issue for hematology laboratories. We aimed at investigating the performance of a powerful computer‐based model to aid diagnosis.
...Materials and methods
Our work presents a new feature selection‐based automated disease diagnosis model. To create a testbed, a new corpus is collected retrospectively. Our data sets contain beta thalassemia trait, iron deficiency anemia, and healthy groups. Our presented automated ailment classification model consists iterative chi2 (IChi2) feature selection and classification phases. The used data set includes 25 features, and IChi2 selects the 20 most valuable of them. These are forwarded to 24 traditional classifiers.
Results
In this work, two data sets have been used to test our proposal. In the classification phase of this model, 24 shallow classifiers have been used and the best accurate classifiers are Medium Gaussian Support Vector Machine (MGSVM) and Coarse Tree (CT) for the first and second data sets, respectively. These classifiers have been attained 97.48% and 99.73% classification accuracies using the first and second data sets, consecutively. These results are calculated using 10‐fold cross‐validation. Moreover, hold‐out validation has been used in this work, and the results are given in the experiments.
Conclusion
Our results denoted the success of IChi2‐based classification model for diagnosis on the laboratory data set. We have found a new and robust model to differentiate iron deficiency anemia and beta thalassemia trait. This model may be beneficial for rational laboratory use.
Brain activities can be evaluated by using Electroencephalogram (EEG) signals. One of the primary reasons for traffic accidents is driver fatigue, which can be identified by using EEG signals. This ...work aims to achieve a highly accurate and straightforward process to detect driving fatigue by using EEG signals. Two main problems, which are feature generation and feature selection, are defined to achieve this aim. This work solves these problems by using two different approaches. Deep networks are efficient feature generators and extract features in low, medium, and high levels. These features can be generated by using multileveled or multilayered feature extraction. Therefore, we proposed a multileveled feature generator that uses a one-dimensional binary pattern (BP) and statistical features together, and levels are created using a one-dimensional discrete wavelet transform (1D-DWT). A five-level fused feature extractor is presented by using BP, statistical features of 1D-DWT together. Moreover, a 2-layered feature selection method is proposed using ReliefF and iterative neighborhood component analysis (RFINCA) to solve the feature selection problem. The goals of the RFINCA are to choose the optimal number of features automatically and use the effectiveness of ReliefF and neighborhood component analysis (NCA) together. A driving fatigue EEG dataset was used as a testbed to denote the effectiveness of eighteen conventional classifiers. According to the experimental results, a highly accurate EEG classification approach is presented. The proposed method also reached 100.0% classification accuracy by using a k-nearest neighborhood classifier.
Nowadays, many people have suffered from sleep disorders. These diseases affect daily life and can disrupt sanity. Sleep disorders/diseases can be diagnosed by using nocturnal sleep sounds. This work ...presents an automated nocturnal sound classification method. The proposed nocturnal sound classification method can be used in the automated sleep disease diagnosis process. To propose a highly accurate and cognitive method, a fused feature generation network is proposed. The proposed fused feature generation network extracts both textural features and statistical features together. Therefore, this method is called as fused textural and statistical feature generation network (FusedTSNet). One-dimensional discrete wavelet transform (DWT) is employed to create levels and 7-leveled DWT is applied to nocturnal sounds. Here, DWT is utilized as a pooling/decomposition method to create a multileveled feature generation network. By using the ReliefF iterative neighborhood component analysis (RFINCA), the most valuable features are selected. To demonstrate the success of the FusedTSNet and RFINCA based nocturnal sound classification method, conventional classifiers are used. The proposed FusedTSNet and RFINCA based nocturnal sound classification method were tested on a collected nocturnal sound dataset. This dataset has 700 sounds in 7 classes. Our method achieved a 98.0% classification rate on this dataset. This work clearly indicates that the automated sleep behavior detection can be developed and the success of the proposed FusedTSNet and RFINCA based sound classification method is obviously shown.
Alzheimer's disease (AD) is one of the most commonly seen brain ailments worldwide. Therefore, many researches have been presented about AD detection and cure. In addition, machine learning models ...have also been proposed to detect AD promptly.
In this work, a new brain image dataset was collected. This dataset contains two categories, and these categories are healthy and AD. This dataset was collected from 1070 subjects. This work presents an automatic AD detection model to detect AD using brain images automatically. The presented model is called a feed-forward local phase quantization network (LPQNet). LPQNet consists of (i) multilevel feature generation based on LPQ and average pooling, (ii) feature selection using neighborhood component analysis (NCA), and (iii) classification phases. The prime objective of the presented LPQNet is to reach high accuracy with low computational complexity. LPQNet generates features on six levels. Therefore, 256 × 6 = 1536 features are generated from an image, and the most important 256 out 1536 features are selected. The selected 256 features are classified on the conventional classifiers to denote the classification capability of the generated and selected features by LPQNet.
The presented LPQNet was tested on three image datasets to demonstrate the universal classification ability of the LPQNet. The proposed LPQNet attained 99.68%, 100%, and 99.64% classification accuracy on the collected AD image dataset, the Harvard Brain Atlas AD dataset, and the Kaggle AD dataset. Moreover, LPQNet attained 99.62% accuracy on the Kaggle AD dataset using four classes.
Moreover, the calculated results from LPQNet are compared to other automatic AD detection models. Comparisons, results, and findings clearly denote the superiority of the presented model. In addition, a new intelligent AD detector application can be developed for use in magnetic resonance (MR) and computed tomography (CT) devices. By using the developed automated AD detector, new generation intelligence MR and CT devices can be developed.
•LPQNet which is a new hand-modeled learning model is proposed in this research.•A new big AD dataset was collected.•Three datasets were used to show universal classification ability of the LPQNet.•LPQNet achieved over 99% classification accuracies on the used all datasets.
Nowadays, many deep models have been presented to recognize emotions using electroencephalogram (EEG) signals. These deep models are computationally intensive, it takes a longer time to train the ...model. Also, it is difficult to achieve high classification performance using for emotion classification using machine learning techniques. To overcome these limitations, we present a hand-crafted conventional EEG emotion classification network. In this work, we have used novel prime pattern and tunable q-factor wavelet transform (TQWT) techniques to develop an automated model to classify human emotions. Our proposed cognitive model comprises feature extraction, feature selection, and classification steps. We have used TQWT on the EEG signals to obtain the sub-bands. The prime pattern and statistical feature generator are employed on the generated sub-bands and original signal to generate 798 features. 399 (half of them) out of 798 features are selected using minimum redundancy maximum relevance (mRMR) selector, and misclassification rates of each signal are evaluated using support vector machine (SVM) classifier. The proposed network generated 87 feature vectors hence, this model is named PrimePatNet87. In the last step of the feature generation, the best 20 feature vectors which are selected based on the calculated misclassification rates, are concatenated. The generated feature vector is subjected to the feature selection and the most significant 1000 features are selected using the mRMR selector. These selected features are then classified using an SVM classifier. In the last phase, iterative majority voting has been used to generate a general result. We have used three publicly available datasets, namely DEAP, DREAMER, and GAMEEMO, to develop our proposed model. Our presented PrimePatNet87 model reached over 99% classification accuracy on whole datasets with leave one subject out (LOSO) validation. Our results demonstrate that the developed prime pattern network is accurate and ready for real-world applications.
•Automated detection of emotion recognition using EEG signals.•Novel prime pattern network is proposed is proposed to generate the features.•Proposed method is developed using three public databases.•Obtained an accuracy of more than 99% for three databases.
•Four pretrained ResNets are utilized to create learning models.•Two ensemble learning models are proposed using two approximations.•Our models are tested on a fire image dataset.•Our models attained ...98.91% and 99.15% accuracies on the used dataset.
Nowadays, fires have been commonly seen worldwide and especially forest fires are big disasters for humanity. The prime objective of this work is to develop an accurate fire warning model by using images. In this work, two new deep feature engineering models are proposed to detect the fire accurately using images. To create deep features, residual networks (ResNet) are chosen since these networks are one of the highly accurate convolutional neural networks. In this work, four pretrained ResNets: ResNet18, ResNet50, ResNet101, and InceptionResNetV2 are used. These networks were trained using a cluster of ImageNet dataset and features were extracted using the last pooling and fully connected layers of these networks. Hence, eight feature vectors are chosen using these networks and the top 256 features of these networks are chosen using neighborhood component analysis (NCA). Support vector machine (SVM) classifier has been used for classification. Moreover, by using the eight feature vectors generated, two ensemble models have been presented. In the first ensemble model, generated all features are concatenated and the top 1000 features are chosen using a feature selector used (NCA), and these features are classified using SVM. In the second ensemble model, iterative hard majority voting (IHMV) has been applied to the generated results. The developed ensemble ResNet models attained 98.91% and 99.15% classification accuracies using an SVM classifier with a 10-fold cross-validation strategy. Our results obtained demonstrate the high classification accuracy of our presented ensemble pretrained ResNet-based deep feature extraction models. These developed models are ready to be tested with higher databases before actual real-world application.
Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver ...fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.
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