Multilayer Neural Network Classifier Research Articles

A Radiomics Model for the Differentiation of Intracranial Solitary Fibrous Tumor/Hemangiopericytoma and Meningioma Based on Multiparametric Magnetic Resonance Imaging.

Although the imaging findings of intracranial solitary fibrous tumor (SFT)/hemangiopericytoma (HPC) and meningioma are similar, their treatment and prognosis are quite different. Accurate preoperative identification of these two types of tumors is crucial for individualized treatment. The aim of this study was to develop a radiomics model for the differentiation of intracranial SFT/HPC and meningioma based on multiparametric magnetic resonance imaging (mpMRI). A total of 99 patients from July 2012 to July 2018 with histologically and immunohistochemically confirmed SFT/HPC (n = 40) or meningiomas (n = 59) were retrospectively analyzed. A total of 1118 features were extracted based on its image shape, intensity and texture features. The logistic regression (LR) and multi-layer artificial neural network (ANN) classifiers were used to classify SFT/HPC and meningioma. The predictive performance was calculated using receiver operating characteristic curves (ROC). We found significant difference in terms of sex between the SFT/HPC and meningioma group (χ2= 4.829, P < 0.05), but no significant difference was found in age (P > 0.05). The most significant radiomics features included five shape and four first-order level features. For the LR classifier, the prediction accuracy of SFT/HPC was 71.0% and meningioma was 78.7%. For the ANN classifier, the prediction accuracy of SFT/HPC was 83.9% and meningioma was 80.9%. Both of the two classifiers achieved a high accuracy rate, but ANN was better. Radiomics features, especially when combined with an ANN classifier, can provide satisfactory performance in distinguishing SFT/HPC and meningioma.

Advancements in remote sensing: Harnessing the power of artificial intelligence for scene image classification

&lt;abstract&gt; &lt;p&gt;The Remote Sensing Scene Image Classification (RSSIC) procedure is involved in the categorization of the Remote Sensing Images (RSI) into sets of semantic classes depending upon the content and this procedure plays a vital role in extensive range of applications, like environment monitoring, urban planning, vegetation mapping, natural hazards' detection and geospatial object detection. The RSSIC procedure exploits Artificial Intelligence (AI) technology, mostly Machine Learning (ML) techniques, for automatic analysis and categorization of the content, present in these images. The purpose is to recognize and differentiate the land cover classes or features in the scene, namely crops, forests, buildings, water bodies, roads, and other natural and man-made structures. RSSIC, using Deep Learning (DL) techniques, has attracted a considerable attention and accomplished important breakthroughs, thanks to the great feature learning abilities of the Deep Neural Networks (DNNs). In this aspect, the current study presents the White Shark Optimizer with DL-driven RSSIC (WSODL-RSSIC) technique. The presented WSODL-RSSIC technique mainly focuses on detection and classification of the remote sensing images under various class labels. In the WSODL-RSSIC technique, the deep Convolutional Neural Network (CNN)-based ShuffleNet model is used to produce the feature vectors. Moreover, the Deep Multilayer Neural network (DMN) classifiers are utilized for recognition and classification of the remote sensing images. Furthermore, the WSO technique is used to optimally adjust the hyperparameters of the DMN classifier. The presented WSODL-RSSIC method was simulated for validation using the remote-sensing image databases. The experimental outcomes infer that the WSODL-RSSIC model achieved improved results in comparison with the current approaches under different evaluation metrics.&lt;/p&gt; &lt;/abstract&gt;

Identifying cancer risks using spectral subset feature selection based on multi-layer perception neural network for premature treatment

Recently, human beings have been affected mainly by dreadful cancer diseases. Predicting cancer risk levels is a major challenge in biomedical research for feature selection and classification at the margins. To resolve this problem, we propose a Subset Clustering-Based Feature Selection using a Multi-Layer Perception Neural Network (SCFS-MLPNN). Initially, pre-processing is carried out with Intensive Mutual Disease Influence Rate (IMDIR) to identify the relational features. In addition, the Successive Disease Pattern Stimulus Rate (SDPSR) is carried out to create relative feature patterns. Based on the patterns, the features are selected and grouped into clustering. Inter-Class Sub-Space Clustering (ICSSC) is applied to split the features by class labels depending on the marginal rate. From the class labels, marginal features are obtained using spectral subset feature selection (SSFS). The selected features are then trained in a Multi-Layer Perception Neural Network (MLPNN) classifier to classify the patient features by risk. Its contribution is to exploit subset features to improve classification accuracy by clustering relational features. The proposed classifier yields higher classification accuracy than previous methods and observes cancer detection for early detection. Therefore, the proposed method achieved a risk analysis accuracy of 91.8% and an F-measure of 91.3% for early detection, which is recommended for early diagnosis.

Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data.

The prediction of prognostic outcome is critical for the development of efficient cancer therapeutics and potential personalized medicine. However, due to the heterogeneity and diversity of multimodal data of cancer, data integration and feature selection remain a challenge for prognostic outcome prediction. We proposed a deep learning method with generative adversarial network based on sequential channel-spatial attention modules (CSAM-GAN), a multimodal data integration and feature selection approach, for accomplishing prognostic stratification tasks in cancer. Sequential channel-spatial attention modules equipped with an encoder-decoder are applied for the input features of multimodal data to accurately refine selected features. A discriminator network was proposed to make the generator and discriminator learning in an adversarial way to accurately describe the complex heterogeneous information of multiple modal data. We conducted extensive experiments with various feature selection and classification methods and confirmed that the CSAM-GAN via the multilayer deep neural network (DNN) classifier outperformed these baseline methods on two different multimodal data sets with miRNA expression, mRNA expression and histopathological image data: lower-grade glioma and kidney renal clear cell carcinoma. The CSAM-GAN via the multilayer DNN classifier bridges the gap between heterogenous multimodal data and prognostic outcome prediction.

A multimodal dual-fusion entity extraction model for large and complex devices

In the context of large and complex devices with a multi-source heterogeneous data environment, the extraction of network device configuration related entity information from diverse modalities of the Internet of Things data is a crucial and fundamental step towards establishing a domain knowledge graph for global Zero Touch Provisioning of network devices. In this paper, we present a novel multimodal dual-fusion entity extraction model that serves as a foundation for an intelligent and efficient network device configuration process. Firstly, the multimodal data is encoded, followed by the ViLBERT pre-training model to obtain more feature information of entities for multimodal front-end fusion. Next, the attention weights of each modal feature are learned through a multilayer neural network classifier and probabilistic graphical model, facilitating multimodal back-end fusion and reducing information redundancy. Finally, entity recognition is accomplished by employing a cohesive memory module that extracts the essential parameters for device configuration. The simulation results demonstrate that the proposed model performs exceptionally well on the MSCOCO2017 public dataset and the SFZK-Dev data network device dataset, with F1 values of the comprehensive evaluation index of model quality at 94.65% and 96.94%, respectively, indicating high stability. Additionally, the link prediction metrics HITS@1 achieved accuracy levels of 58.21% and 68.04%.

Smart paradigm to predict copper surface area of Cu/ZnO/Al2O3 catalyst based on synthesis parameters

Cu/ZnO/Al2O3 catalyst is used in processes of water-gas shift, methanol steam reforming, and methanol synthesis in the industry. According to various experimental studies, the catalytic activity of this catalyst is directly proportional to its copper surface area (Cu(SA)). In this study, a machine learning approach for predicting Cu(SA) ranges in three classes (low, medium, and high) is introduced based on catalyst preparation factors. Three models of random forest (RF), support vector machine (SVM), and multilayer perceptron artificial neural network (MLP-ANN) classifiers are developed and optimized using grid search 10-fold cross-validation for a 188 sample dataset extracted from 45 experimental studies. It is found that the RF classifier with 90% cross-validation accuracy score and 94.7% test data prediction accuracy score outperforms the other two models. The SHAP (or SHapley Additive exPlanations) analysis is performed to investigate the effects of synthesis factors, such as aging conditions, precipitant type, and pH on Cu(SA). It is concluded that Cu/Zn ratio has the greatest influence on Cu(SA). The optimum synthesis conditions yielding high Cu(SA) are also discovered, which is of great importance for synthesis of Cu/ZnO/Al2O3 catalysts with high catalytic activity.

Diagnosis of Pulpitis from Dental Panoramic Radiograph Using Histogram of Gradients with Discrete Wavelet Transform and Multilevel Neural Network Techniques

Radiograph plays the major role of diagnosis, treatment and surgery in the Dental field. There are many types of Intra and extra oral radiographs in which Dental Panoramic Radiograph helps in visualising the full view of the oral cavity. Pulpitis is the dental diseases caused due to the inflammation of the dental pulp from untreated caries, trauma or multiple restorations which leads to Apical Periodontitis. To predict the severity of pulp vitality pulp inflammation has to be evaluated. Radiographs helps the dentist in diagnosing the extent of tooth decay and inflammation. An automatic diagnostic model is proposed using robust algorithms to diagnose pulpits. Dental Panoramic Radiograph is used in the proposed research to diagnose the pulpitis and to classify the normal teeth from the pulpitis. The collected images are pre-processed using Histogram Equalization and filtered using Gaussian and Median filters. Modified K-Means algorithm is applied to segment the bony and teeth area from the oral cavity area. Integral Histogram of Gradients with Discrete Wavelet Transform feature extraction techniques and Multi-Layer Neural Network Classifier is proposed to achieve the accuracy of 91.09% which can be used as an assistive tool by the dentist to diagnose pulpitis.

A Comprehensive Computer-Assisted Diagnosis System for Early Assessment of Renal Cancer Tumors

Renal cell carcinoma (RCC) is the most common and a highly aggressive type of malignant renal tumor. In this manuscript, we aim to identify and integrate the optimal discriminating morphological, textural, and functional features that best describe the malignancy status of a given renal tumor. The integrated discriminating features may lead to the development of a novel comprehensive renal cancer computer-assisted diagnosis (RC-CAD) system with the ability to discriminate between benign and malignant renal tumors and specify the malignancy subtypes for optimal medical management. Informed consent was obtained from a total of 140 biopsy-proven patients to participate in the study (male = 72 and female = 68, age range = 15 to 87 years). There were 70 patients who had RCC (40 clear cell RCC (ccRCC), 30 nonclear cell RCC (nccRCC)), while the other 70 had benign angiomyolipoma tumors. Contrast-enhanced computed tomography (CE-CT) images were acquired, and renal tumors were segmented for all patients to allow the extraction of discriminating imaging features. The RC-CAD system incorporates the following major steps: (i) applying a new parametric spherical harmonic technique to estimate the morphological features, (ii) modeling a novel angular invariant gray-level co-occurrence matrix to estimate the textural features, and (iii) constructing wash-in/wash-out slopes to estimate the functional features by quantifying enhancement variations across different CE-CT phases. These features were subsequently combined and processed using a two-stage multilayer perceptron artificial neural network (MLP-ANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype as well. Using the combined features and a leave-one-subject-out cross-validation approach, the developed RC-CAD system achieved a sensitivity of , a specificity of , and Dice similarity coefficient of in differentiating malignant from benign tumors, as well as an overall accuracy of in discriminating ccRCC from nccRCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The obtained results using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, relational functional gradient boosting, etc.). Hence, integrating morphological, textural, and functional features enhances the diagnostic performance, making the proposal a reliable noninvasive diagnostic tool for renal tumors.

Sound-spectrogram based automatic bird species recognition using MLP classifier

Bioacoustics play a major role in the field of ornithology, ecology, animal behaviour study, habitat monitoring, species conservation and design of deterrent system. This work focusses on the design and implementation and performance evaluation of an automatic, more efficient and flexible bird sound based recognition system for classifying eight species of popular Eurasian birds the standard online annotated databases. A dedicated virtual instrument tool with effective GUI is developed that acquires, pre-processes the sound samples and generates statistically evaluated short term Fourier transform spectrogram based feature matrix, suited for characterization of vocalization patterns of bird species. Using the well-labelled feature data of sound records, multi-layer perceptron artificial neural network (MLP-NN) classifier model is designed, trained, tested and optimized using feedforward-backpropagation supervised learning algorithm. Various experiments, following a systematic approach, are conducted to optimize the structure of MLP with respect of number of neurons in the hidden layer, epochs and learning rate for attaining enhanced recognition accuracy (96.1%), recall (82.6%) and precision (84.5%). The performance of the optimal model is also analysed in terms of recognition capabilities of individual bird species that indicate promising results. A few of the birds are recognized accurately and precisely when present as compared to the others. The tendency of the model to wrongly identify or miss bird species also remained low. The model performance over the unseen dataset also remained satisfactory with cross-validation classification accuracy of 81.4%. The system being scalable, can easily be reused in future to retrain the model over the large set of sound samples from real world recordings with improved acoustic features for achieving very high classification accuracy and reliability.

A Power System Disturbance Classification Method Robust to PMU Data Quality Issues

Data quality issues exist in practical phasor measurement units (PMUs) due to communication errors or signal interferences. As a result, the performances of existing data-driven disturbance classification methods can be significantly affected. In this article, a fast disturbance classification method that is robust to PMU data quality issues is proposed. The impacts of bad PMU measurements on disturbance classification are investigated by analyzing the feature distributions of deep learning methods. A new feature extraction scheme that uses the univariate temporal convolutional denoising autoencoder (UTCN-DAE) is proposed. It allows encoding and decoding univariate disturbance data through a temporal convolutional network to capture the temporal feature representation and is robust to bad data. Based on the features of the frequency and voltage measurements encoded by the UTCN-DAE, a two-stream enhanced network, i.e., the multivariable temporal convolutional denoising network is proposed to achieve optimal feature extraction of multivariate time series by feature fusion. The classification is performed using a multilayered deep neural network and Softmax classifier. Extensive results obtained on the IEEE 39-bus system as well as a large-scale power system in China with field PMU measurements show that the proposed method achieves the highest classification accuracy and computational efficiency as compared to other deep learning algorithms.

Intelligent robot chair with communication aid using TEP responses and higher order spectra band features

In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.

Hybrid clustering system using Nystagmus parameters discrimination for vestibular disorder diagnosis.

The control of clinical manifestation of vestibular system relies on an optimal diagnosis. This study aims to develop and test a new automated diagnostic scheme for vestibular disorder recognition. In this study we stratify the Ellipse-fitting technique using the Video Nysta Gmographic (VNG) sequence to obtain the segmented pupil region. Furthermore, the proposed methodology enabled us to select the most optimum VNG features to effectively conduct quantitative evaluation of nystagmus signal. The proposed scheme using a multilayer neural network classifier (MNN) was tested using a dataset involving 98 patients affected by VD and 41 normal subjects. The new MNN scheme uses only five temporal and frequency parameters selected out of initial thirteen parameters. The scheme generated results reached 94% of classification accuracy. The developed expert system is promising in solving the problem of VNG analysis and achieving accurate results of vestibular disorder recognition or diagnosis comparing to other methods or classifiers.

Intelligent Robotic Chair With Thought Control and Communication Aid Using Higher Order Spectra Band Features

In recent years, EEG-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked sensor potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order robotic sensor spectra-based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i.e. Delta, Theta, Alpha, Beta, Gamma1-1 and Gamma-2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the mean of bispectral magnitude and entropy using the bispectrum region are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71% and the mean of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.

A phase entropy based novel machine learning structure conditioned for classifying ictal and non-ictal signal aimed at proper clinical diagnosis

Proposed scheme is based on the detection and classification of ictal and pre-ictal electro-encephalogram (EEG) signal of a partially affected epileptic patients. This work helps for the detection of ictal EEG signal as compared to pre-ictal EEG signal. In this work a conceptual method is used for the perfect detection of seizures. Here phase entropies (PEnS1 and PEnS2) are followed for feature extraction ictal and pre-ictal EEG signals. Again, extracted features are classified through multi-layer perception neural network (MLPNN) classifier tool. To train and test the classifier, map reduce quantum PSO (MRQPSO) is used. By considering various statistical parameters like Accuracy, Sensitivity, Specificity, positive predictive value, negative predictive value and Matthew's correlation coefficient, the performance of the proposed scheme has evaluated.

Detection of Breast Cancer by Thermal Based Sensors using Multilayered Neural Network Classifier

Consideration of public health problem issues, one of the most common diseases in public is cancer. Most of the women population is suffering from breast cancer which is the most well known appearance of cancer in metropolitan cities of India and abroad. There many number of imaging modalities to diagnose cancerous cells. Among those, mammography is alone an imaging modality which diagnoses the breast cancer at an early stage. Furthermore, this modality involves X-rays which are more harmful to human health and make the patient inconvenience. Through the mammogram, doctors can analyze, estimate and evaluate the cancer stage so that doctors can give better and correct treatment to the patients. With this mortality and death rates can also be diminished up to some extent. In this paper, the author proposed an intelligent system to identify and find out the severity of breast cancer. By using a thermal based sensor which is of negative Temperature Coefficient (NTC) available with C-MET Thrissur which replaces Mammography. The stage at which the cancer is progressing is classified with the help of Intelligent System Algorithms which works on the temperature data obtained from the thermal device. The data is pre-processed and applied to multilayered backpropogation neural network model. The neural network classifies the preprocessed images into normal, benign and cancer. The output of the network is presented to the doctors through graphs and displays.

Stomach Deformities Recognition Using Rank-Based Deep Features Selection.

Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15s.

Modified spatio-temporal neural networks for failure risk prognosis and status forecasting of permanent downhole pressure gauge

Permanent Downhole Pressure Gauges (PDPGs) are sensors installed in the wellbores to measure the downhole pressure. Due to the abrupt changes in pressure, the PDPG can fail, and this will result in difficulties in production optimization. To manage the operational risk levels (negligible, low, mild, high and severe), a signal processing technique that utilizes anomaly detection, Fisher's Discriminant Function (FDF) and Mean Deviant Function (MDF) is developed. The operational risk levels were established with the Bayesian Gaussian Mixed Model (BGMM) and a Spatiotemporal Graphical Model (STGM) helped to compute the risks transition probabilities at different time intervals. After balancing the resulting input-output data with Synthetic Minority Oversampling Technique (SMOTE) and using Multi-layer Perceptron Artificial Neural Network (MLP-ANN) classifier, an online PDPG monitoring strategy was developed. The model was tested with Coal Seam Gas (CSG) PDPG data from thirty-eight wells and the results showed an average accuracy of ~93%. This result indicates the potency of the model for managing the risk profile of PDPGs and other telemetric equipment used for critical missions in the oil and gas, space, medical and defence industries.

Recognition of Odia Handwritten Digits using Gradient based Feature Extraction Method and Clonal Selection Algorithm

This article aims to recognize Odia handwritten digits using gradient-based feature extraction techniques and Clonal Selection Algorithm-based (CSA) multilayer artificial neural network (MANN) classifier. For the extraction of features which contribute the most towards recognition from images, are extracted using gradient-based feature extraction techniques. Principal component analysis (PCA) is used for dimensionality reduction of extracted features. A MANN is used as a classifier for classification purposes. The weights of the MANN are adjusted using the CSA to get optimized set of weights. The proposed model is applied on Odia handwritten digits taken from the Indian Statistical Institution (ISI), Calcutta, which consists of four thousand samples. The results obtained from the experiment are compared with a genetic-based multi-layer artificial neural network (GA-MANN) model. The recognition accuracy of the CSA-MANN model is found to be 90.75%.

An efficient 3D color-texture feature and neural network technique for melanoma detection

Abstract Malignant melanoma is the deadliest form of skin cancer, but can be more readily treated successfully if detected in its early stages. Due to the increasing incidence of melanoma, research in the field of autonomous melanoma detection has accelerated. In this paper, a new method for feature extraction from dermoscopic images, termed multi-direction 3D color-texture feature (CTF), is proposed, and detection is performed using a back propagation multilayer neural network (NN) classifier. The proposed method is tested on the PH2 dataset (publicly available) in terms of accuracy, sensitivity, and specificity. The extracted combined CTF is fairly discriminative. When it is input and tested in a neural network classifier that is provided, encouraging results are obtained, i.e. accuracy = 97.5%, sensitivity = 98.1% and specificity = 93.84%. Comparative result analyses with other methods are also discussed, and the results are also improved over benchmarking results for the PH2 dataset.

Blind source separation by multilayer neural network classifiers for spectrogram analysis

Blind source separation by multilayer neural network classifiers for spectrogram analysis