Unmarked Samples Research Articles

On Noise Effect in Semi-supervised Learning

The article deals with the problem of noise effect on semi-supervised learning. The goal of this article is to analyze the impact of noise on the accuracy of binary classification models created using three semi-supervised learning algorithms, namely Simple Recycled Selection, Incrementally Reinforced Selection, and Hybrid Algorithm, using Support Vector Machines to build a base classifier. Different algorithms to compute similarity matrices, namely Radial Bias Function, Cosine Similarity, and K-Nearest Neighbours were analyzed to understand their effect on model accuracy. For benchmarking purposes, datasets from the UCI repository were used. To test the noise effect, different amounts of artificially generated randomly-labeled samples were introduced into the dataset using three strategies (labeled, unlabeled, and mixed) and compared to the baseline classifier trained with the original dataset and the classifier trained on the reduced-size original dataset. The results show that the introduction of random noise into the labeled samples decreases classifier accuracy, while a moderate amount of noise in unmarked samples can have a positive effect on classifier accuracy.

Rumour influence minimization and topic modelling for twitter dataset using machine learning schemes

We advocate a joint combined trend sentiment categorization method to guide sentiment classifiers for a couple of tweets at the same time. Distinctively, putrefy the sentiment classifier of all trends linked to two additives, namely the specific trend and the global trend. Our method gives a green way to as it should sort out trendy subjects devoid of the lack of outside facts making news channels to find out the infringement news in a particular time or to rush out viral memes to enhance marketing decisions with competitors. The analysis of social functions also reveals styles associated with all kinds of trends, such as tweets with approximately ongoing activities of trendsetters. The unique version of the Trends Greedy and Dynamic Blocking Algorithms can capture the precise expressions of sentiment in each Trend. In addition, we extract trends unique sentimental knowledge from each of the classified and unmarked samples in each Trend and use it to embellish the mastery of Trends precise sentiment classifiers. In addition, we are introducing green algorithms to resolve the version of our technique. These schemes can effectively boost the performance of the combined trends group and outperform traditional methods with experimental effects on benchmark datasets.

Depth Prototype Clustering Method Based on Unsupervised Field Alignment for Bearing Fault Identification of Mechanical Equipment

In the process of mechanical equipment fault diagnosis, it is difficult to obtain enough labeled samples due to the changeable operating conditions, complex working environment, and the limitation of measuring equipment. Therefore, a prototype clustering method based on unsupervised domain alignment is proposed for fault detection and diagnosis of mechanical equipment under unmarked samples. First, the relative maximum mean discrepancy index is constructed to map the data collected from different devices to the same high-dimensional space for feature alignment. Then, cluster the output and calculate the prototype for each category. Compared with the classification directly using the softmax function, the prototype clustering method can correct the local possible misjudged points through the overall average distribution. By minimizing the global distance error, prototypes obtained by clustering in the target domain are matched with prototypes of tagged samples in the source domain and, finally, realize unsupervised state recognition. Six experiments are designed to verify the effectiveness of the proposed method. The accuracy of the proposed method is 1%–2% higher even when compared to one of the most advanced fault diagnosis methods available. Also, it can be found from the results of t-stochastic neighbor embedding (SNE) visualization that the proposed method has higher knowledge transfer capability.

A Study of an Engine Anomaly Detection Model IForest-ADOA

In the environment of the Industrial Internet, automatic and effective anomaly detection methods are of great importance to ensure the safety and stability of engines. However, in practical engine applications, traditional machine learning anomaly detection methods often have limitations due to a large number of unmarked samples and high latitude data. In this paper, an engine anomaly detection model based on the ADOA model is proposed, combining the unsupervised learning iForest model with the semi-supervised learning ADOA model. Through the anomalous samples detected by iForest, the ADOA model is used to learn their features and patterns and perform clustering, to achieve the anomaly detection of the samples and improve its accuracy at the same time. Experiments based on a real engine dataset show that this model has good results in terms of anomaly recognition rate and anomaly misclassification rate for anomaly detection of high-dimensional data generated by real environments.

Active Learning for Visual Image Classification Method Based on Transfer Learning

The active learning method involves searching for the most informative unmarked samples by query function, submitting them to the expert function for marking, then using the samples to train the classification model in order to improve the accuracy of the model and use the newly acquired knowledge to inquire into the next round, with the aim of getting the highest accuracy of classification using minimal training samples. This paper details the various principles of active learning and develops a method that combines active learning with transfer learning. Experimental results prove that the active learning method can cut back on samples redundancy and promote the accuracy of classifier convergence quickly in small samples. Combining active learning and transfer learning, while taking advantage of knowledge in related areas, could further improve the generalization ability of classification models.

A Collaborative Study on a Method to Detect Salmonella in Food

Fourteen laboratories with expertise in Salmonella detection in food joined in a collaborative study. The laboratories performed qualitative analyses of ground pork samples using the proposed detection method. Salmonella Typhimurium (hydrogen sulfide-producing strain) and Salmonella Senftenberg (hydrogen sulfide-nonproducing strain) were used for inoculation. Three levels of Salmonella contamination were used for the study (0, 1-10, and 11-100 cfu/25 g). We evaluated the presence of Salmonella in each sample and the serological O group. Unmarked samples delivered to the laboratories were accurately judged to be inoculated or not inoculated with Salmonella at a 99.8% (419/420) detection rate in this collaborative study. The proposed method is suitable as a standard method to detect Salmonella in food.

Summer‐Run Hatchery Steelhead Have Naturalized in the South Fork Skykomish River, Washington

AbstractEvaluation of natural‐origin, hatchery‐origin, and unmarked steelhead Oncorhynchus mykiss from the Skykomish River drainage basin, Washington, was conducted to determine the source of unmarked steelhead that return to Sunset Falls (South Fork Skykomish River). One possible source is the large number of steelhead stocked into the Skykomish River basin from Reiter Ponds Hatchery; this hatchery stock was founded with fish from Skamania Hatchery in the Washougal River system, Washington. A microsatellite DNA analysis of 10 loci was used to evaluate unmarked samples in comparison with natural‐origin samples from the North Fork Skykomish River and hatchery‐origin samples from Reiter Ponds Hatchery. Results of the analyses provide evidence that the unmarked steelhead collected at Sunset Falls are more closely related to Reiter Ponds Hatchery fish than to natural‐origin fish from the North Fork Skykomish River. There is evidence that unmarked steelhead at Sunset Falls are also mixing with natural‐origin North Fork Skykomish River fish but to a lesser degree than with Reiter Ponds Hatchery fish. This study documents that Skamania Hatchery‐origin steelhead have naturally produced offspring that are returning to spawn in a northern Puget Sound river basin.

Xanthrochromia? By What Method? A Comparison of Visual and Spectrophotometric Xanthrochromia

Most US hospitals use visual inspection for the detection of cerebrospinal fluid xanthrochromia. We compared visual inspection with spectrophotometric xanthrochromia and studied the effect of tube diameter on the sensitivity of visual inspection. Blinded, experienced laboratory technicians visually examined unmarked samples to determine the presence or absence of xanthrochromia. Samples were prepared by lysing RBCs in distilled water. Serial dilutions were placed in clear polystyrene tubes obtained from standard lumbar puncture trays. Laboratory technicians were asked to examine each sample for xanthrochromia using visual inspection. Next, they were asked to interpret the same set of samples with the assistance of a threshold standard. Last, they were asked to interpret the same dilutions, but this time presented in a larger-diameter tube. The absorbance of each sample was measured in a double-beam spectrophotometer at wavelengths between 300 and 700 nm. Samples were said to demonstrate spectrophotometric xanthrochromia if they had an absorbance greater than 0.023 at 415 nm. Sixteen laboratory technicians were shown a total of 160 samples, of which 64 (40%) demonstrated spectrophotometric xanthrochromia. Visual inspection of the samples was 26.6% sensitive (95% confidence interval [CI] 16% to 38%) and 97.9% specific (95% CI 95% to 100%) for spectrophotometric xanthrochromia. Using a reference standard did not improve the performance of visual inspection, but increasing collection-tube diameter increased the sensitivity to 55% (95% CI 43% to 68%). Visual inspection is not sensitive for the detection of spectrophotometric xanthrochromia. Increasing the diameter of the collection tubes did improve sensitivity. Emergency physicians should be aware of how xanthrochromia is determined at their institutions and understand the implications of using visual detection to determine the presence or absence of xanthrochromia.

Xanthrochromia: By which method? A comparison of the visual and spectrophotometric determination of xanthrochromia

Study objectives: Although spectrophotometric definitions of xanthrochromia exist, nearly all hospitals in the United States use visual inspection for the detection of xanthrochromia. We compare visual inspection for xanthrochromia to the spectrophotometric detection of xanthrochromia. Methods: Laboratory technicians from a tertiary care hospital visually examined unmarked samples for the presence or absence of xanthrochromia. Samples were prepared by vigorously mixing 3 mL of blood with 3 mL of deiodinated water to lyse the RBCs. Dilutions were then made ranging from 1 in 5,000 to 1 in 100,000. Samples of each dilution were placed into clear polystyrene tubes obtained from lumbar puncture trays. In a random sequence, laboratory technicians visually examined each sample for xanthrochromia. In addition, each sample was scanned using a double-beam spectrophotometer (Spectronic Genesis 5, Spectronic Instruments, Rochester, NY) at wavelengths between 300 nm and 700 nm. Absorption was recorded for peaks at 415 nm in allergenic units, the resonant wavelength for oxyhemoglobin. Samples were considered to demonstrate xanthrochromia by spectrophotometry if their absorbance was greater than 0.023 allergenic units at 415 nm. Results: Sixteen laboratory technicians examined a total of 160 samples, of which 64 (40%) demonstrated spectrophotometric xanthrochromia. Visual inspection of the samples was 27% sensitive (95% confidence interval [CI] 16% to 39%) and 98% specific (95% CI 93% to 100%) for spectrophotometric xanthrochromia. Conclusion: Visual inspection is not sensitive for the detection of xanthrochromia. Emergency physicians should understand the limitations of the visual technique in their interpretation of the cerebrospinal fluid analysis of potential subarachnoid hemorrhage patients.

The Characterization of Electrical Insulating Oils by High-Performance Liquid Chromatography

A new method for the characterization of electrical insulating oils, which employs the recently-developed technique of high-performance liquid chromatography, is presented. The method is simple and rapid. It provides of oils, with information about their molecular size distribution, aromatic types and their degree of alkylation. These fingerprints can be used to identify unmarked samples of oils and to differentiate, for the purposes of fault diagnosis, the typical degradations (e. g. polymerization or cracking) undergone by oils in service.

Survival and behaviour of tsetse flies (Diptera, Glossinidae) released in the field: a comparison between wild flies and animal-fed and in vitro-fed laboratory-reared flies

AbstractMale and female Glossina morsitans morsitans Westw. which emerged from puparia produced by animal-fed and in vitro-fed colonies in England were marked distinctively with non-toxic paint and released into a natural habitat of G. morsitans and G. pallidipes Aust. in Rhodesia. Concurrently, adults of both species which emerged from locally-collected puparia were marked and released. Recaptures from artificial refuges, odour attractants and mobile baits at periods up to 59 days after release and at distances up to 1800 m from the release site indicated no clear differences between native G. morsitans and the two laboratory-reared groups in respect of body size, amount of fat present at emergence, survival, dispersal, availability to a range of baits, diet, speed of taking a first meal, wing damage and insemination rate. Although the blood-meal identifications for marked female G. morsitans were similar to those for both sexes of unmarked flies, blood-meals from marked males showed a relatively high proportion of bovid identifications. Unmarked flies caught were generally older than marked catches. The ratio of females to males in unmarked samples (1:1 for G. morsitans, 2:1 for G. pallidipes) was roughly double that in marked catches.