Liver tumor detection and segmentation using kernel-based extreme learning machine

Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search

Pulp concentration is one of the most important production parameters during ore dressing process. Generally, pulp concentration not only affects concentrate grade and recovery rate, but also has a major influence on the chemical and power consumptions during the flotation process. Recently, there has been a growing interest in the study of prediction for pulp concentration to improve the productivity and reduce consumption of various resources. Since the pulp concentration and other production parameters are nonlinearly related, it imposes very challenging obstacles to the prediction for this parameter. Because extreme learning machine (ELM) has the advantages of extremely fast learning speed, good generalization performance, and the smallest training errors, we employ ELM to predict pulp concentration in this paper. Pulp concentration data is first preprocessed using phase space reconstruction method. Then time series prediction model is adjusted from one dimension to multiple dimensions and thus it is established by several improved ELM algorithms, including traditional ELM, kernel-based ELM (Kernel-ELM), regularized ELM (R-ELM), and \(L_2\)-norm based ELM (ELM-L2). The experiments are conducted with a real-world production data set from a mine. The experimental results show the effectiveness of ELM-based prediction approaches, and we can also find that ELM-L2 has better prediction effects than other algorithms with the increase of sample size. Both training speed and prediction accuracy are improved by employing ELM-L2 to the prediction of pulp concentration.

Clinical texts are inherently complex due to the medical domain expertise required for content comprehension. In addition, the unstructured nature of these narratives poses a challenge for automatically extracting information. In natural language processing, the use of word embeddings are an effective approach to generate word representations (vectors) in a low dimensional space. In this paper we use a log-linear model (a type of neural language model) and Linear Discriminant Analysis with a kernel-based Extreme Learning Machine (ELM) to map the clinical texts to the medical code. Experimental results on clinical texts indicate improvement with ELM in comparison to SVM and neural network approaches.

Monitoring and controlling microclimate in a greenhouse becomes one of the research hotspots in the field of agrometeorology, where the application of Wireless Sensor Networks (WSN) recently attracts more attentions due to its features of self-adaption, resilience and cost-effectiveness. Present microclimate monitoring and control systems achieve their prediction by manipulating captured environmental factors and traditional neural network algorithms; however, these systems suffer the challenges of quick prediction (e.g. hourly and even minutely) when a WSN network is deployed. In this paper, a novel prediction method based on an Extreme Learning Machine (ELM) algorithm and KELM (Kernel based ELM) is proposed to predict the temperature and humidity in a practical greenhouse environment in Nanjing, China. Indoor temperature and humidity are measured as data samples via WSN nodes. According to the results, our approach (0.0222s) has shown significant improvement on the training speed than Back Propagation (Bp) (0.7469s), Elman (11.3307s) and Support Vector Machine (SVM) (19.2232s) models, the accuracy rate of our model is higher than those models. In the future, research on faster learning speed of the ELM and KELM based neural network model will be conducted.

Known as an ancient civilization, there exists a large amount of earthen archaeological sites in China. Various types of environment monitoring data have been accumulated waiting to be analyzed for the aim of future protection. In this paper, a non-stationary data processing strategy is proposed for the better understanding of such monitoring data. The kernel-based extreme learning machine (ELM) is utilized to preprocess the original data and restore the missing parts. Then a new non-uniform sampling time-frequency representation (TFR) is proposed to analyze the non-stationary characteristic of restored data from a signal processing perspective. The test data is the real environment monitoring data of the burial pit at the Yang Mausoleum of the Han dynasty. The experimental result shows that the proposed scheme can extract different information from the original data.

Forecasting solar radiation for a given region is an emerging field of study. It will help to identify the places for installing large-scale photovoltaic-systems, designing energy-efficient buildings and energy estimation. The different machine learning kernel-based approaches for prediction problems uses either a local or global kernel. These models can provide either strong training capability or good generalization performance. In this paper, a new hybrid kernel is proposed using the combination of a local and global kernel. A novel algorithm Hybrid Kernel-based Extreme Learning Machine is proposed for predictive modelling of Incident Solar Radiation (ISR) time series using new hybrid kernel. The proposed algorithm uses the surface, atmospheric, cloud properties obtained from the MODIS instrument and observed ISR at time t – 1 to predict ISR at time t. This study is conducted for 41 diverse sites of Australia for the period of 2012–2015. Further, the proposed model is experimented with other time series datasets to prove its efficacy. It is shown that the proposed methodology outperforms five other benchmarking methods in terms of MAE and Willmott’s Index (WI). Therefore, the suggested approach can be used for modelling solar energy at a national scale using remotely-sensed satellite footprints.

Gas turbine combustor anomaly detection plays a critical role in reducing operation and maintenance costs in power plant operations. One-class classification, to learn a model that properly describes the normal samples, is one of many anomaly detection approaches that have shown promising performance in real-world applications. Extreme learning machines (ELMs), a recent developed machine learning technique, have outperformed SVMs and other machine learning methods for many machine learning problems. ELMs have been originally used for binary or multiclass classification and regression as well. Using ELM for one-class classification has emerged very recently, but has not been widely explored for real world applications. In this paper, we adopt ELMs to a new application domain - industrial machine condition monitoring. More specifically, we apply one-class ELMs for more accurate anomaly detection of gas turbine combustors. We evaluate different one-class ELMs and compare them against other one-class classifiers. Using a real-world gas turbine combustor anomaly detection as the case study, we demonstrate that one-class ELMs can be more effective than other one-class classification algorithms in early detecting gas turbine combustor anomalies.

Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields.

Landslide displacement prediction is an essential component in landslide early warning system. The displacement prediction based on in-suit monitoring performs excellently but is expensive, which limited its promotion in less-developing regions. In this study, we propose a cost-effective landslide displacement prediction method with the combination of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique and Artificial Bee Colony and Kernel-based Extreme Learning Machine (ABC-KELM). Two large slow-moving landslides in the Three Gorges Reservoir area, namely Shuping landslide and Muyubao landslide, are selected as study cases. We first extract the surface displacement sequences of both landslides through the MT-InSAR and the spaceborne Copernicus Sentinel-1 SAR imagery. The original displacement sequences are decomposed into trend terms, periodic terms, and noise using wavelet analysis. The modelling inputs of trend and periodic displacements are determined by analyzing the relationship between their influencing factors and deformation. The trend and periodic displacement are respectively predicted using ABC-KELM, and summing both predicted displacement to get total displacement. By comparing the displacement obtained by the Global Positioning System in both landslides, we find the MT-InSAR can monitor landslide displacement accurately. Prediction results demonstrate that the ABC algorithm can effectively optimize the parameters of the KELM. ABC-KELM outperforms the commonly used algorithms of extreme learning machine and support vector machine. Its root mean square error, relation coefficient, and mean absolute percentage error is 5.460, 0.022, and 0.990, respectively. Our proposed method is cost-effective in landslide displacement prediction, which can be recommended in susceptible regions.

Traffic sign recognition plays an important role in autonomous vehicles as well as advanced driver assistance systems. Although various methods have been developed, it is still difficult for the state-of-the-art algorithms to obtain high recognition precision with low computational costs. In this paper, based on the investigation on the influence that color spaces have on the representation learning of convolutional neural network, a novel traffic sign recognition approach called DP-KELM is proposed by using a kernel-based extreme learning machine (KELM) classifier with deep perceptual features. Unlike the previous approaches, the representation learning process in DP-KELM is implemented in the perceptual Lab color space. Based on the learned deep perceptual feature, a kernel-based ELM classifier is trained with high computational efficiency and generalization performance. Through the experiments on the German traffic sign recognition benchmark, the proposed method is demonstrated to have higher precision than most of the state-of-the-art approaches. In particular, when compared with the hinge loss stochastic gradient descent method which has the highest precision, the proposed method can achieve a comparable recognition rate with significantly fewer computational costs.

In this paper, we propose an approach to detection and segmentation of liver tumors in noisy computed topography (CT) images. Image segmentation plays crucial role in medical image processing applications. Noise is quite common in medical images. It occurs while acquisition and transmission of images. The essential goal of this framework is to identify liver cancer by segmenting liver tumors from noisy CT scan images. Liver segmentation is difficult task in medical applications because inter-patient variability in size, shape and disease. In general CT scanning is used to inspect liver cancer. In this research work, the liver tumors are detected by the medical images in three stages, pre-processing stage, processing stage and detection stage. First in pre-processing stage, median filter is used to remove the noise from CT image, and then the denoised image is segmented by fuzzy c-means clustering (FCM) algorithm. Finally in the detection stage distance regularized level set evolution (DRLSE) is used to extract tumor boundaries. This algorithm is very much useful for identifying hepatocellular carcinoma (liver cancer). Experimental results on various noisy CT scan images show that the proposed method is efficient for extracting hepatic tumors from liver.

A composite framework coupling multiple feature selection, compound prediction models and novel hybrid swarm optimizer-based synchronization optimization strategy for multi-step ahead short-term wind speed forecasting

Cancer plays a major risk for public health worldwide. According to the survey made by the cancer society predicts approximate about 42,220 new cases will be diagnosed and around 32,220 people will die of this cancer that is around 71% of people will die in 2018 and Liver cancer rate is increased by 3% for every year since 2000 and achieved second leading place for the cause of death. There is a con-tinuous in the development with regard to prevent and different options for treating the cancer. Detection of cancer at its initial stages is very difficult with the help of pathological information’s, so as any added support CAD systems using CT scan images are being designed from few decades in order to find out cancer in its early stage. In this paper discussed various segmentation techniques and liver tumor detection techniques to initial segment out the liver region from the abdominal and then to extract the efficient characteristics. Based on the characteristics presences of tumour is identified and separated out from the liver and finally analyse the stage of the cancer. Therefore the process is divided into three parts; 1.Region segmentation, 2.Liver Tumour segmentation and 3.Detection of Cancer stage. In this paper, study is done on different methods of liver region and tumour segmentation of abdominal CT scan to analyze liver tumor and detection of early stage of the tumor

The Phase-Shift Flip-Flop (PS-FF) is the key component or module of power sources, and its fault leads to abnormal operation or accidents. However, there are few investigations on full fault analysis and there are fewer effective fault methods. In this paper, the typical PS-FFs used in the rod-controlled power source of a nuclear action station is taken as an example, the fault effects and modes are analysed, and then a diagnosis method based on the Kernel based Extreme Learning Machine (KELM) model and the Improved Black-winged Kite Optimization Algorithm (IBKA) is proposed. The vulnerable components of PS-FF and its failure effects are first analysed, and then the fault mode and external characteristics are analysed by dividing PS-FF into the phase-shift trigger module and the pulse drive module according to the circuit function of PS-FF. By comparing the external characteristics, new features and extraction methods from the time-domain and frequency domains are proposed for PS-FF diagnosis and the separability of faults is verified with circuit simulation analysis. Then, by combining the KELM and IBKA methods and using the online collection of phase-shifted voltage signals, the IBKA-KELM model diagnosis method is proposed to detect and classify faults of PS-FF online. The simulation and experimental results show that this proposed method can diagnose PS-FF faults and has a high accuracy rate.

The operational land imager (OLI) is the latest instrument in the Landsat series of satellite imagery, which officially began normal operations on 30 May 2013. The OLI includes two bands that are not on the thematic mapper series of sensors aboard Landsat-5 and 7; a cirrus band and a coastal/aerosol band. This paper compares the classification and regression tree and the kernel-based extreme learning machine (KELM) for mapping crops in Hokkaido, Japan, using OLI data, except the cirrus band and the pan band. The OLI data acquired on 8 July 2013 was used for crop classification of beans, beets, grassland, maize, potatoes and winter wheat. The KELM algorithm performed better in this study and achieved overall accuracies of 90.1%. According to the Jeffries–Matusita (J–M) distances, the short wavelength infrared band provides the greater contribution (the highest value was observed for band 6 in OLI data).