Combination Of Statistical Features Research Articles

Information Analysis of Advanced Mathematics Education-Adaptive Algorithm Based on Big Data

With the rapid development of artificial intelligence (AI) concept technology, it promotes the innovation of educational concept. Mostly for the education information analysis in the class of mathematics in the university, it should be based on a big data-driven system to promote the quality of teaching in the classroom. In the method of teaching math in university, teachers should take full advantage of the benefits of a big data-driven system powered by AI, grow a good teaching model for students, promote education through big data, progressively teach students according to their aptitude, develop in a tailored direction, increase teaching quality and effectiveness, and finally create more great talents for our country. For the sake of improving the resource sharing and the management level of the curriculum which teaches advanced knowledge about mathematics teaching, based on a particle swarm optimization algorithm, an advanced math teaching system is proposed in this paper. The fusion model that can be used in the teaching process of math in university is constructed, the adaptive scheduling of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resources is realized by an optimization algorithm used for fusion particle swarm, the autocorrelation feature of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resources is extracted, and the adaptive optimization of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching resource fusion is realized by fuzzy correlation feature matching and statistical analysis. In the process of particle swarm optimization, the combination of statistical features is studied and managed, the resource scheduling and information fusion are realized, and the management capability of the curriculum which teaches advanced knowledge about mathematics auxiliary teaching is promoted, and the experimental results demonstrate that the designed system has a high integration of teaching information resources and strong information scheduling ability and improved the management level of the curriculum which teaches advanced knowledge about mathematics complementary teaching.

A deep matrix factorization framework for identifying underlying tissue-specific patterns of DCE-MRI: applications for molecular subtype classification in breast cancer

Objective. Breast cancer is heterogeneous in that different angiogenesis and blood flow characteristics could be present within a tumor. The pixel kinetics of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assume several distinct signal patterns related to specific tissue characteristics. Identification of the latent, tissue-specific dynamic patterns of intratumor heterogeneity can shed light on the biological mechanisms underlying the heterogeneity of tumors. Approach. To mine this information, we propose a deep matrix factorization-based dynamic decomposition (DMFDE) model specifically designed according to DCE-MRI characteristics. The time-series imaging data were decomposed into tissue-specific dynamic patterns and their corresponding proportion maps. The image pixel matrix and the reference matrix of population-level kinetics obtained by clustering the dynamic signals were used as the inputs. Two multilayer neural network branches were designed to collaboratively project the input matrix into a latent dynamic pattern and a dynamic proportion matrix, which was justified using simulated data. Clinical implications of DMFDE were assessed by radiomics analysis of proportion maps obtained from the tumor/parenchyma region for classifying the luminal A subtype. Main results. The decomposition performance of DMFDE was evaluated by the root mean square error and was shown to be better than that of the conventional convex analysis of mixtures (CAM) method. The predictive model with K = 3, 4, and 5 dynamic proportion maps generated AUC values of 0.780, 0.786 and 0.790, respectively, in distinguishing between luminal A and nonluminal A tumors, which are better than the CAM method (AUC = 0.726). The combination of statistical features from images with different proportion maps has the highest prediction value (AUC = 0.813), which is significantly higher than that based on CAM. Conclusion. This proposed method identified the latent dynamic patterns associated with different molecular subtypes, and radiomics analysis based on the pixel compositions of the uncovered dynamic patterns was able to determine molecular subtypes of breast cancer.

Abstract 255: TransVAF: A transfer learning approach for recognize genomic mutations with various tumor purity and clonal proportions

Abstract Mutation detection, via genomic sequencing, has become a routine in cancer diagnosis and precision treatments. Although the existing bioinformatics approaches alter the algorithms and strategies for the detection, they always rely on the combinations of statistical features on a set of overlapped reads to recognize the real mutations from false positives. It may be the Achilles heel of such recognition mechanisms, that their preset thresholds are often stiff and ambiguous and the feature combinations with adaptive thresholds could only be enabled by machine learning frameworks. Unfortunately, the learning models have to overcome the challenges caused by tumor heterogeneity. For mutations from different subclones, most of the sequencing features associate with the tumor purity and clonal proportions. It introduces complicated interactions among the features and breaks the independent co-distribution assumption of classic learning models. In addition, both the tumor purity and clonal proportions are variables, thus, any training sets cannot enumerate all possible values. These challenges hurt the specificities of the existing approaches applying to cancer sequencing data. Here, we propose an approach for the scenario of various tumor purity and clonal proportions. The proposed approach incorporates a comprehensive set of the features according to the existing strategies. Then, it requires at least two training sets with different proportions. For any given set, we have fixed tumor purity and clonal proportions. The framework first trains the models according to one set. The trained models focus on the associations between the features and true mutations. These models are defined as a source domain. Next, when the other set is input for training, the framework not only trains another source domain, but focuses on the transformations among the features between the source domains as well. Now, when another fixed tumor purity and clonal proportions are considered, the framework is able to generate, maybe roughly, the models for the new group of the purity and proportions according to the source domains and the transformations. To enhance the performance, we propose to integrate a few source domains to control the systematic errors during the transfer processes. The Boyer-Moore majority-vote algorithm is introduced to achieve the integration. We have carried out a series of experiments on both simulated and real datasets, and compared to the state-of-the-art approaches, including MuTect2, Sentieon, VarScan2, Freebayes and SiNVICT. The results demonstrated that the proposed method adapts well to different diluted sequencing signals and can significantly reduce the false positive. It is implemented as TransVAF. The software package has been uploaded at https://github.com/TrinaZ/TL-fpFilter for academic usage only. Citation Format: Tian Zheng, Jiayin Wang, Xiao Xiao, Xiaoyan Zhu, Xuanping Zhang, Xin Lai, Yanfang Guan, Xin Yi. TransVAF: A transfer learning approach for recognize genomic mutations with various tumor purity and clonal proportions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 255.

Comparison of Machine Learning Algorithms for the Quality Assessment of Wearable ECG Signals Via Lenovo H3 Devices

PurposeElectrocardiogram (ECG) signals collected from wearable devices are easily corrupted with surrounding noise and artefacts, where the signal-to-noise ratio (SNR) of wearable ECG signals is significantly lower than that from hospital ECG machines. To meet the requirements for monitoring heart disease via wearable devices, eliminating useless or poor-quality ECG signals (e.g., lead-falls and low SNRs) can be solved by signal quality assessment algorithms.MethodsTo compensate for the deficiency of the existing ECG quality assessment system, a wearable ECG signal dataset from heart disease patients collected by Lenovo H3 devices was constructed. Then, this paper compares the performance of three machine learning algorithms, i.e., the traditional support vector machine (SVM), least-squares SVM (LS-SVM) and long short-term memory (LSTM) algorithms. Different non-morphological signal quality indices (i.e., the approximate entropy (ApEn), sample entropy (SaEn), fuzzy measure entropy (FMEn), Hurst exponent (HE), kurtosis (K) and power spectral density (PSD) features) extracted from the original ECG signals are fed into the three algorithms as input.ResultsThe true positive rate, true negative rate, sensitivity and accuracy are used to evaluate the performance of each method, and the LSTM algorithm achieves the best results on these metrics (97.14%, 86.8%, 97.46% and 95.47%, respectively).ConclusionsAmong the three algorithms, the LSTM-based quality assessment method is the most suitable for the signals collected by the Lenovo H3 devices. The results also show that the combination of statistical features can effectively evaluate the quality of ECG signals.

Blind image quality assessment using a combination of statistical features and CNN

Blind Image Quality Assessment (BIQA) has been an enticing research problem in image processing, during the last few decades. In spite of the introduction of several BIQA algorithms, quantifying image quality without the help of a reference image still remains an unsolved problem. We propose a method for BIQA, combining Natural Scene Statistics (NSS) feature and Probabilistic Quality representation by a CNN. A certain number of features are considered for each image. We also propose to increase the NSS feature set alongside with the same CNN architecture and compare its results accordingly. Support Vector Machine (SVM) regression is applied on these features to get a quality score for that particular image. The results obtained by applying the proposed quality score on benchmark datasets, show the effectiveness of the proposed quality metric compared to the state-of-the-art metrics.

Markov random field classification technique for plant leaf disease detection

In recent era of technology, computer vision technique has grown attraction of the researchers. This technique helps to identify and classify the objects according to the application requirement. These techniques are widely used for plant leaf detection and helping to develop an automated process for plant leaf disease detection. A new approach is developed in this work for plant leaf disease detection using Markov random classification technique. MRF-based problem is formulated for disease detection. In the next stage, the general stages of computer vision classification model i.e., pre-processing and feature extraction is applied. For pre-processing, noise removal and image enhancement models are applied and feature extraction is combination of statistical features. Neighborhood pixel modeling and MRF classification models are applied to obtain the classification of input data. Performance of three classification models is compared. Study shows that proposed approach gives robust performance for plant leaf disease detection and classification.

Markov random field classification technique for plant leaf disease detection

In recent era of technology, computer vision technique has grown attraction of the researchers. This technique helps to identify and classify the objects according to the application requirement. These techniques are widely used for plant leaf detection and helping to develop an automated process for plant leaf disease detection. A new approach is developed in this work for plant leaf disease detection using Markov random classification technique. MRF-based problem is formulated for disease detection. In the next stage, the general stages of computer vision classification model i.e., pre-processing and feature extraction is applied. For pre-processing, noise removal and image enhancement models are applied and feature extraction is combination of statistical features. Neighborhood pixel modeling and MRF classification models are applied to obtain the classification of input data. Performance of three classification models is compared. Study shows that proposed approach gives robust performance for plant leaf disease detection and classification.

Disaster damage assessment from the tweets using the combination of statistical features and informative words

Nowadays, Twitter has become more popular among the users for communicating the information, especially during disaster. Identifying tweets related to the target event during disaster is a challenging task. Many prior studies discussed situational and non-situational information related to disaster. The detection of tweets related to damage assessment is a very difficult task in social media because it is a subset of situational information. One of the following drawbacks has been present in the existing damage assessment works: (1) focused only on infrastructure damage but does not include human damage in the assessment, (2) focused only on social media image data for damage assessment and (3) focused only on regional language tweets. To overcome these issues, Stacking-based Ensemble using Statistical features and Informative Words (SESIW) is proposed for detecting the tweets related to damage assessment. It uses proposed features, namely frequency of hashtags, user mentions, wh-words, URLs, count of numerals and informative words. Informative words are mined using term frequency and inverse document frequency technique. The SESIW method is tested on different Twitter disaster datasets, and it outperforms the baseline SVM with Bag-of-Words model.

TR-IDS: Anomaly-Based Intrusion Detection through Text-Convolutional Neural Network and Random Forest

As we head towards the IoT (Internet of Things) era, protecting network infrastructures and information security has become increasingly crucial. In recent years, Anomaly-Based Network Intrusion Detection Systems (ANIDSs) have gained extensive attention for their capability of detecting novel attacks. However, most ANIDSs focus on packet header information and omit the valuable information in payloads, despite the fact that payload-based attacks have become ubiquitous. In this paper, we propose a novel intrusion detection system named TR-IDS, which takes advantage of both statistical features and payload features. Word embedding and text-convolutional neural network (Text-CNN) are applied to extract effective information from payloads. After that, the sophisticated random forest algorithm is performed on the combination of statistical features and payload features. Extensive experimental evaluations demonstrate the effectiveness of the proposed methods.

Automation of multi-fault diagnosing of centrifugal pumps using multi-class support vector machine with vibration and motor current signals in frequency domain

Reliable detection and isolation of centrifugal pump (CP) faults is a challenging and important task in the modern industries. Hence, this paper proposes an artificial intelligence-based multi-fault detection of CPs driven by induction motor. The intelligent fault detection methodology is developed based on the multi-class support vector machine (MSVM). The mechanical and hydraulic faults in CPs are mutually dependent and therefore may exist concurrently. Hence, in the present research, an assortment of various flow instabilities like the suction re-circulation, discharge re-circulation, pseudo-re-circulation and dry runs are considered coexisting with mechanical faults, like the impeller cracks and pitted cover plate faults. The power spectrum of the CP vibration and the induction motor line-current data is used for monitoring the CP condition. The best statistical feature combination is selected based on a wrapper model. Gaussian radial basis function (RBF) is used for the kernel mapping. In addition, the RBF kernel parameter (width) and MSVM parameters are optimally selected using a fivefold cross-validation technique. Also, variation of operating speed of the CP drastically changes the system vibration level owing to the change in fault manifestations; hence, in the present work a methodology that is independent of CP operation is proposed and tested. Thereafter, it is observed that the proposed methodology is remarkably robust and successfully classifies multiple individual as well as coexisting CP faults at all the tested CP speeds.

Feature selection for surface defect classification of extruded aluminum profiles

This research investigates detection and classification of two types of the surface defects in extruded aluminium profiles; blisters and scratches. An experimental system is used to capture images and appropriate statistical features from a novel technique based on gradient-only co-occurrence matrices (GOCM) are proposed to detect and classify three distinct classes; non-defective, blisters and scratches. The developed methodology makes use of the Sobel edge detector to obtain the gradient magnitude of the image (GOCM). A comparison is made between the statistical features extracted from the original image (GLCM) and those extracted from the gradient magnitude (GOCM). This paper describes in detail every step of the image processing with example pictures illustrating the methodology. The features extracted from the image processing are classified by a two-layer feed-forward artificial neural network. The artificial neural network training is tested using different combinations of statistical features with different topologies. Features are compared individually and grouped. Results are discussed, achieving up to 98.6 % total testing accuracy.

Effectiveness of Statistical Features for Human Emotions Classification using EEG Biosensors

This study proposes a statistical features-based classification system for human emotions by using Electroencephalogram (EEG) bio-sensors. A total of six statistical features are computed from the EEG data and Artificial Neural Network is applied for the classification of emotions. The system is trained and tested with the statistical features extracted from the psychological signals acquired under emotions stimulation experiments. The effectiveness of each statistical feature and combinations of statistical features in classifying different types of emotions has been studied and evaluated. In the experiment of classifying four main types of emotions: Anger, Sad, Happy and Neutral, the overall classification rate as high as 90% is achieved.

Artificial Intelligence Based Green Technology Retrofit for Misfire Detection in Old Engines

The core theme of the paper is misfire detection using random forest algorithm and decision tree based machine learning models for emission minimization in gasoline passenger vehicles. The engine block vibration signals are used for misfire detection. The signal is a combination of all vibration emissions of various engine components and also contains the vibration signature due to misfire. The quantum of information available at a given instant is enormous and hence suitable techniques are adopted to reduce the computational load due to redundant information. The random forest algorithm based model and the decision tree model are found to have a consistent high classification accuracy of around 89.7% and 89.3% respectively. From the results obtained the authors conclude that the combination of statistical features and random forest algorithm is suitable for detection of misfire in spark ignition engines and hence contributing to emission minimization in vehicles.