Analysis Of Multiple Features Research Articles

Artificial intelligence-based classification of breast nodules: a quantitative morphological analysis of ultrasound images.

Accurate classification of breast nodules into benign and malignant types is critical for the successful treatment of breast cancer. Traditional methods rely on subjective interpretation, which can potentially lead to diagnostic errors. Artificial intelligence (AI)-based methods using the quantitative morphological analysis of ultrasound images have been explored for the automated and reliable classification of breast cancer. This study aimed to investigate the effectiveness of AI-based approaches for improving diagnostic accuracy and patient outcomes. In this study, a quantitative analysis approach was adopted, with a focus on five critical features for evaluation: degree of boundary regularity, clarity of boundaries, echo intensity, and uniformity of echoes. Furthermore, the classification results were assessed using five machine learning methods: logistic regression (LR), support vector machine (SVM), decision tree (DT), naive Bayes, and K-nearest neighbor (KNN). Based on these assessments, a multifeature combined prediction model was established. We evaluated the performance of our classification model by quantifying various features of the ultrasound images and using the area under the receiver operating characteristic (ROC) curve (AUC). The moment of inertia achieved an AUC value of 0.793, while the variance and mean of breast nodule areas achieved AUC values of 0.725 and 0.772, respectively. The convexity and concavity achieved AUC values of 0.988 and 0.987, respectively. Additionally, we conducted a joint analysis of multiple features after normalization, achieving a recall value of 0.98, which surpasses most medical evaluation indexes on the market. To ensure experimental rigor, we conducted cross-validation experiments, which yielded no significant differences among the classifiers under 5-, 8-, and 10-fold cross-validation (P>0.05). The quantitative analysis can accurately differentiate between benign and malignant breast nodules.

Telomere and subtelomere high polymorphism might contribute to the specificity of homologous recognition and pairing during meiosis in barley in the context of breeding

Barley (Hordeum vulgare) is one of the most popular cereal crops globally. Although it is a diploid species, (2n = 2x = 14) the study of its genome organization is necessary in the framework of plant breeding since barley is often used in crosses with other cereals like wheat to provide them with advantageous characters. We already have an extensive knowledge on different stages of the meiosis, the cell division to generate the gametes in species with sexual reproduction, such as the formation of the synaptonemal complex, recombination, and chromosome segregation. But meiosis really starts with the identification of homologous chromosomes and pairing initiation, and it is still unclear how chromosomes exactly choose a partner to appropriately pair for additional recombination and segregation. In this work we present an exhaustive molecular analysis of both telomeres and subtelomeres of barley chromosome arms 2H-L, 3H-L and 5H-L. As expected, the analysis of multiple features, including transposable elements, repeats, GC content, predicted CpG islands, recombination hotspots, G4 quadruplexes, genes and targeted sequence motifs for key DNA-binding proteins, revealed a high degree of variability both in telomeres and subtelomeres. The molecular basis for the specificity of homologous recognition and pairing occurring in the early chromosomal interactions at the start of meiosis in barley may be provided by these polymorphisms. A more relevant role of telomeres and most distal part of subtelomeres is suggested.

Soil carbon content prediction using multi-source data feature fusion of deep learning based on spectral and hyperspectral images

Visible near-infrared reflectance spectroscopy (VNIR) and hyperspectral images (HSI) have their respective advantages in soil carbon content prediction, and the effective fusion of VNIR and HSI is of great significance for improving the prediction accuracy. But the contribution difference analysis of multiple features in the multi-source data is inadequate, and there is a lack of in-depth research on the contribution difference analysis of artificial feature and deep learning feature. In order to solve the problem, soil carbon content prediction methods based on VNIR and HSI multi-source data feature fusion are proposed. The multi-source data fusion network under the attention mechanism and the multi-source data fusion network with artificial feature are designed. For the multi-source data fusion network based on the attention mechanism, the information are fused through the attention mechanism according to the contribution difference of each feature. For the other network, artificial feature are introduced to fuse multi-source data. The results show that multi-source data fusion network based on the attention mechanism can improve the prediction accuracy of soil carbon content, and multi-source data fusion network combined with artificial feature has better prediction effect. Compared with two single-source data from the VNIR and HSI, the relative percent deviation of Neilu, Aoshan Bay and Jiaozhou Bay based on multi-source data fusion network combined with artificial feature are increased by 56.81% and 149.18%, 24.28% and 43.96%, 31.16% and 28.73% respectively. This study can effectively solve the problem of the deep fusion of multiple features in the soil carbon content prediction by VNIR and HSI, so as to improve the accuracy and stability of soil carbon content prediction, promote the application and development of soil carbon content prediction in spectral and hyperspectral image, and provide technical support for the study of carbon cycle and the carbon sink.

Pragmatic Evidence on Android Malware Analysis Techniques: A Systematic Literature Review

Alarge number of studies including research articles and surveys on android malware detection and analysis techniques have been presented during the last one and a half decades. The authors proposed different systems and frameworks to identify malware from software applications. However, there is no recent and comprehensive systematic literature review on the detection and analysis of android malware methods, systems, and frameworks. We present a systematic review of literature on android malware detection and analysis techniques and tools by following standard guidelines for Systematic Literature Review methodology from 2010 to 2021. We selected 75 most relevant studies out of 3343 published studies. We found that the prominent malicious datasets are Genome (39%) and Drebin (36%) used by different researchers for the detection of malware. The static, dynamic, and hybrid source code analysis methods are applied by android malware detection techniques. We also identified the limitations and future research directions of existing techniques as research gaps for the community. Based on the pragmatic evidence of this research, we have proposed a hybrid analysis-based multiple feature analysis framework. This framework will not only address the limitations of static and dynamic-based approaches, but it also analyzes evolving android malware datasets using deep neural network and machine learning techniques and improve the accuracy of evolving malware samples.

A Heuristic Video Recommendation Algorithm based on Similarity Computation for Multiple Features Analysis

Objective:: The short video applications have achieved great success in recent years. The number of videos being shot and uploaded to these platforms has increased greatly. In this way, mining and recommending videos for users based on their interests have become a very difficult problem in these video distribution platforms. Under this case, it becomes particularly important to design efficient video recommendation algorithms for these platforms. In order to solve the problem faced by high sparsity and large scale data sets in the field of media big data mining and recommendation, a heuristic video recommendation algorithm for multi-dimensional feature analysis and filtering is proposed. Methods:: Firstly, the video features are extracted from multiple dimensions such as user behavior and video tags. Then, the similarity analysis is carried out, and the video similarity degree is calculated by weighting, so as to obtain the similar video candidate set, and filter the similar video candidate set. After that, the videos with the highest scores are recommended to users by sorting. Finally, the video recommendation algorithm proposed in this paper is implemented by using the C language. Results:: Compared with the benchmark, the proposed video recommendation algorithm has improved the accuracy by 6.1%-136.4%, the recall rate by 19.3%-30.9%, the coverage rate by 55.6%-59.5%, the running time by 42.7%-60.4%, and the cache hit ratio by 10.9%-47.4%. Conclusion:: The proposed algorithm can effectively and greatly improve the accuracy, recall rate, coverage rate, running time and cache hit ratio.

Supervised Learning Aided Multiple Feature Analysis for Freshness Class Detection of Indian Gooseberry (Phyllanthus emblica)

Supervised Learning Aided Multiple Feature Analysis for Freshness Class Detection of Indian Gooseberry (Phyllanthus emblica)

Radiomic Detection of EGFR Mutations in NSCLC

Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of this study was to develop a predictive algorithm to define the mutational status of EGFR in treatment-naïve patients with advanced non-small cell lung cancer (NSCLC). CT scans from 109 treatment-naïve patients with NSCLC (21 EGFR-mutant and 88 EGFR-wild type) underwent radiomics analysis to develop a machine learning model able to recognize EGFR-mutant from EGFR-WT patients via CT scans. A "test-retest" approach was used to identify stable radiomics features. The accuracy of the model was tested on an external validation set from another institution and on a dataset from the Cancer Imaging Archive (TCIA). The machine learning model that considered both radiomic and clinical features (gender and smoking status) reached a diagnostic accuracy of 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy values in the datasets from TCIA and the external institution were 76.6% and 83.3%, respectively. Furthermore, 17 distinct radiomics features detected at baseline CT scan were associated with subsequent development of T790M during treatment with an EGFR inhibitor. In conclusion, our machine learning model was able to identify EGFR-mutant patients in multiple validation sets with globally good accuracy, especially after data optimization. More comprehensive training sets might result in further improvement of radiomics-based algorithms. SIGNIFICANCE: These findings demonstrate that data normalization and "test-retest" methods might improve the performance of machine learning models on radiomics images and increase their reliability when used on external validation datasets.

Multiparameter Approach for Damage Propagation Analysis in Fiber-Reinforced Polymer Composites

Assessing the damage evolution in carbon-fiber-reinforced polymer (CFRP) composites is an intricate task due to their complex mechanical responses. The acoustic emission technique (AE) is a non-destructive evaluation tool that is based on the recording of sound waves generated inside the material as a consequence of the presence of active defects. Proper analysis of the recorded waves can be used for monitoring the damage evolution in many materials, including composites. The acoustic track associated with the entire loading history of the sample or the structures is usually followed by using some descriptors, such as the amplitude of the sound waves and the number of counts. In this study, the acoustic emission in CFRP single-lap shear joints was monitored by using a multiparameter approach based on the contemporary analysis of multiple features, such as the absolute signal level (ASL), initiation frequency, and reverberation frequency, to understand whether a proper combination of them can be adopted for a more robust description of the damage propagation in CFRP structures. For selecting the best features, principal component analysis (PCA) was used. The selected features were classified into different clusters using fuzzy c-means (FCM) data clustering for analyzing the damage modes.

Deep learning for early performance prediction of introductory programming students: a comparative and explanatory study

Introductory programming may be complex for many students. Moreover, there is a high failure and dropout rate in these courses. A potential way to tackle this problem is to predict student performance at an early stage, as it facilitates human-AI collaboration towards prescriptive analytics, where the instructors/monitors will be told how to intervene and support students - where early intervention is crucial. However, the literature states that there is no reliable predictor yet for programming students’ performance, since even large-scale analysis of multiple features have resulted in only limited predictive power. Notice that Deep Learning (DL) can provide high-quality results for huge amount of data and complex problems. In this sense, we employed DL for early prediction of students’ performance using data collected in the very first two weeks from introductory programming courses offered for a total of 2058 students during 6 semesters (longitudinal study). We compared our results with the state-of-the-art, an Evolutionary Algorithm (EA) that automatic creates and optimises machine learning pipelines. Our DL model achieved an average accuracy of 82.5%, which is statistically superior to the model constructed and optimised by the EA (p-value << 0.05 even with Bonferroni correction). In addition, we also adapted the DL model in a stacking ensemble for continuous prediction purposes. As a result, our regression model explained ~62% of the final grade variance. In closing, we also provide results on the interpretation of our regression model to understand the leading factors of success and failure in introductory programming.

Automatic Detection of Dissimilar Regions through Multiple Feature Analysis

Automatic Detection of Dissimilar Regions through Multiple Feature Analysis

Infrared Small Target Detection Through Multiple Feature Analysis Based on Visual Saliency

Infrared small target detection in extreme environments such as low illumination or complex background with low signal clutter ratio is of crucial significance and counted as a difficult task in infrared search and tracking systems. In this paper, an effective infrared small target detection method is proposed based on the human visual system characteristics and multiple feature analysis. By using the contrast mechanism and visual attention mechanism, spatial gray level-based feature map and saliency extraction based feature map from frequency domain are obtained. Based on the characteristics of infrared dim target images and human visual attention mechanism, the target saliency features are revealed through the feature analysis in the spatial domain and frequency domain respectively. The saliency features from each feature map are applied to the final saliency map. By this means, the background clutter and noise are inhibited and the targets are distinct for the various scenes in the infrared images. The experimental results show that the proposed method has a robust and effective performance in terms of detection and false alarm rates. Comparing with the other methods in the experiments, the proposed method is feasible and adaptable in the various scenes of infrared images.

Multiple Feature Analysis for Infrared Small Target Detection

Detection of small target has been an important and challenging task in infrared systems. Most detection algorithms which only use single metric are difficult to separate target from clutter completely. The false alarm may be high when there exists complex backgrounds. In this letter, multiple novel features are proposed from four aspects to establish elaborate description. Each feature reflects specific characteristic of small target. The best feature vector is selected to apply these features for detection. Then, learning-based classifier is trained to screen candidate targets which are obtained by initial segmentation. Experimental results demonstrate that the proposed features could discriminate small targets from various clutters effectively. The better detection performance is achieved compared with other methods in different infrared backgrounds.

Building-Based Damage Detection From Postquake Image Using Multiple-Feature Analysis

Damaged building detection from high spatial resolution remote sensing image helps to rapid disaster losses assessment. However, the majority of traditional methods relies on only a single category feature of the damaged building. This letter presents a new strategy for detecting damaged buildings from postquake remote sensing image by multiple-feature analysis, in which the integrity of the building edge and the interior roof was both considered. The intactness of the building edge was assessed by proposing a new feature parameter, edge significance (ES), ES using significance test to quantify the difference between the gradient values on the edge and in the edge buffer. In addition, the gradient orientation inside the building was analyzed and local gradient orientation entropy (LOE) parameter was adopted to determine whether the interior roof was damaged. In general, damaged buildings have lower ES values because of broken edges and higher LOE values owing to debris, final decision was made on the basis of both feature parameters. A Quickbird image of Yushu, China, was used in the experiment and, among a total of 327 buildings, 266 were detected correctly. The overall accuracy was 84.10%, which is better than traditional methods.

Leaf classification using multiple feature analysis based on semi-supervised clustering

Multiple features such as the margin, the shape and the texture of plant leaves are of great importance for classification of plant species, as they are often regarded as the unique features to identify plants. In this paper, we study the performance of a recently proposed semi-supervised fuzzy clustering algorithm with feature discrimination for leaf classification, based on features generated by principal component analysis of color images. The method outlines a basic framework for judging the weights of different features by adopting multiple feature matrixes obtained from the initial images as input data and the clustering results of the proposed clustering algorithm as output data to distinguish dissimilarities between various leaves. Real leaf images are employed to evaluate its performance and the experiment demonstrates that these results suggest that the margin feature, the shape feature and combination feature especially the margin feature and combination feature may be the best choice for leaf classification.

A multistage mathematical approach to automated clustering of high-dimensional noisy data

A critical problem faced in many scientific fields is the adequate separation of data derived from individual sources. Often, such datasets require analysis of multiple features in a highly multidimensional space, with overlap of features and sources. The datasets generated by simultaneous recording from hundreds of neurons emitting phasic action potentials have produced the challenge of separating the recorded signals into independent data subsets (clusters) corresponding to individual signal-generating neurons. Mathematical methods have been developed over the past three decades to achieve such spike clustering, but a complete solution with fully automated cluster identification has not been achieved. We propose here a fully automated mathematical approach that identifies clusters in multidimensional space through recursion, which combats the multidimensionality of the data. Recursion is paired with an approach to dimensional evaluation, in which each dimension of a dataset is examined for its informational importance for clustering. The dimensions offering greater informational importance are given added weight during recursive clustering. To combat strong background activity, our algorithm takes an iterative approach of data filtering according to a signal-to-noise ratio metric. The algorithm finds cluster cores, which are thereafter expanded to include complete clusters. This mathematical approach can be extended from its prototype context of spike sorting to other datasets that suffer from high dimensionality and background activity.

Differential diagnosis of cervical spinal cord demyelinating diseases and cervical intramedullary gliomas

Objective To analyze the imaging characteristics of cervical spinal cord demyelinating diseases and cervical intramedullary gliomas, so as to improve the differential diagnosis between them. Methods A retrospective analysis was conducted using clinical and MRI data from 22 cases of cervical spinal cord demyelinating diseases and 16 cases of cervical intramedullary gliomas. Results Clinical features in both groups included paresthesia [77.27% (17/22), 12/16], weakness of limbs [72.73% (16/22), 10/16], and dysfunction of autonomic nerve [45.45% (10/22), 4/16]. In cervical MRI, the lesions involving more than 3 vertebras were 63.64% (14/22) in demyelinating group and 15/16 in glioma group, and the average lengths of lesions were (3.41 ± 1.74) and (3.59 ± 1.28) vertebras in 2 groups. The lesions showed long T 1 signal [68.18% (15/22), 7/16], equisignal T 1 [31.82% (7/22), 6/16] and long T 2 signal [100% (22/22), 8/15] in 2 groups. Mixed T 1 and T 2 signals (3/16, 6/15) could be seen in glioma group. Demyelinating lesions had unclear boundary [90.91% (20/22)] with patchy and ribbon-like enhancement (13/16). Limited enlargement of spinal cord (15/16) and thickening spinal meninges (14/16) were more common in glioma group, usually with block and circular enhancement (12/16). Spinal cord involvement around central canal could be seen (14/15), and the cysts or central canal enlargement, hemorrhage and cap sign were showed frequently (7/16, 5/16 and 4/16). Conclusions Although none of one single clinical or MRI feature was sufficient enough to identify cervical spinal demyelinating diseases from cervical glioma, the comprehensive analysis of multiple features could help to make differential diagnosis of these diseases. doi: 10.3969/j.issn.1672-6731.2014.09.008

Predicting the Lifespan and Retweet Times of Tweets Based on Multiple Feature Analysis

In social network services, such as Facebook, Google+, Twitter, and certain postings attract more people than others. In this paper, we propose a novel method for predicting the lifespan and retweet times of tweets, the latter being a proxy for measuring the popularity of a tweet. We extract information from retweet graphs, such as posting times; and social, local, and content features, so as to construct prediction knowledge bases. Tweets with a similar topic, retweet pattern, and properties are sequentially extracted from the knowledge base and then used to make a prediction. To evaluate the performance of our model, we collected tweets on Twitter from June 2012 to October 2012. We compared our model with conventional models according to the prediction goal. For the lifespan prediction of a tweet, our model can reduce the time tolerance of a tweet lifespan by about four hours, compared with conventional models. In terms of prediction of the retweet times, our model achieved a significantly outstanding precision of about 50%, which is much higher than two of the conventional models showing a precision of around 30% and 20%, respectively.

Semi-Supervised Multiple Feature Analysis for Action Recognition

This paper presents a semi-supervised method for categorizing human actions using multiple visual features. The proposed algorithm simultaneously learns multiple features from a small number of labeled videos, and automatically utilizes data distributions between labeled and unlabeled data to boost the recognition performance. Shared structural analysis is applied in our approach to discover a common subspace shared by each type of feature. In the subspace, the proposed algorithm is able to characterize more discriminative information of each feature type. Additionally, data distribution information of each type of feature has been preserved. The aforementioned attributes make our algorithm robust for action recognition, especially when only limited labeled training samples are provided. Extensive experiments have been conducted on both the choreographed and the realistic video datasets, including KTH, Youtube action and UCF50. Experimental results show that our method outperforms several state-of-the-art algorithms. Most notably, much better performances have been achieved when there are only a few labeled training samples.

Packet Loss Concealment Algorithm Based on Robust Voice Classification in Noise Environment

실시간 VoIP 네트워크는 지연, 지터 그리고 패킷손실과 같은 네트워크 장애요소로 인해 품질저하가 발생한다. 본 논문은 VoIP 음질 향상을 위해 잡음환경에 강인한 음성분류기반의 패킷손실 은닉 알고리즘을 제안한다. 제안된 방식에서는 음성신호로부터 추출된 다양한 특징들을 분석하고 이를 기반으로 획득된 적응적인 문턱값을 사용하여 수신단에 도착한 패킷을 분류한다. 정확한 신호분류 결과는 패킷손실 은닉에 사용된다. 그리고 선형 예측 기반의 손실패킷 은닉은 연속적으로 패킷을 은닉하거나 손실된 패킷복원 시 발생하는 메탈릭 아티펙트를 제거함으로써 고품질의 음성을 제공한다. The quality of real-time Voice over Internet Protocol (VoIP) network is affected by network impariments such as delays, jitters, and packet loss. This paper proposes a packet loss concealment algorithm based on voice classification for enhancing VoIP speech quality. In the proposed method, arriving packets are classified by an adaptive thresholding approach based on the analysis of multiple features of short signal segments. The excellent classification results are used in the packet loss concealment. Additionally, linear prediction-based packet loss concealment delivers high voice quality by alleviating the metallic artifacts due to concealing consecutive packet loss or recovering lost packet.

Increased mean lung density: Another independent predictor of lung cancer?

ObjectivesTo investigate the relationship between emphysema phenotype, mean lung density (MLD), lung function and lung cancer by using an automated multiple feature analysis tool on thin-section computed tomography (CT) data. MethodsBoth emphysema phenotype and MLD evaluated by automated quantitative CT analysis were compared between outpatients and screening participants with lung cancer (n=119) and controls (n=989). Emphysema phenotype was defined by assessing features such as extent, distribution on core/peel of the lung and hole size. Adjusted multiple logistic regression models were used to evaluate independent associations of CT densitometric measurements and pulmonary function test (PFT) with lung cancer risk. ResultsNo emphysema feature was associated with lung cancer. Lung cancer risk increased with decreasing values of forced expiratory volume in 1s (FEV1) independently of MLD (OR 5.37, 95% CI: 2.63–10.97 for FEV1<60% vs. FEV1≥90%), and with increasing MLD independently of FEV1 (OR 3.00, 95% CI: 1.60–5.63 for MLD>−823 vs. MLD<−857 Hounsfield units). ConclusionEmphysema per se was not associated with lung cancer whereas decreased FEV1 was confirmed as being a strong and independent risk factor. The cross-sectional association between increased MLD and lung cancer requires future validations.