Traditional Datasets Research Articles

Based on Baidu Index and GBDT Shanghai Index rise and fall forecast

The stock market reflects the country's economic conditions, and it is of great significance to have a good prediction effect on the stock market. But with the rapid rise of the Internet, big data, and machine learning, the prediction of the stock market trend is not limited to the traditional methods and data sets. The trend of the stock market is not only dependent on itself but also affected by some other factors. Therefore, based on the machine learning model, this paper studies the prediction of investors' attention to the Shanghai Composite Index trend. This paper crawled the relevant index data from the website of Baidu Index based on the selected keywords. The correlation coefficient is used to select the keyword data with the best lag order and data type and as the model's input data. Through the establishment of LSTM, LASSO, RF, and GBDT models, the rise and fall of the Shanghai Composite Index are predicted. That is to say. The paper takes the accuracy of the rise and fall prediction as the judgment standard. GBDT model has the best prediction effect on the Shanghai Stock Exchange Index and can best explain the rise and fall of the Shanghai Stock Exchange Index. So, people can use this research to buy stocks before they rise and sell them before they fall.

Semantic Segmentation of Hyperspectral Remote Sensing Images Based on PSE-UNet Model.

With the development of deep learning, the use of convolutional neural networks (CNN) to improve the land cover classification accuracy of hyperspectral remote sensing images (HSRSI) has become a research hotspot. In HSRSI semantics segmentation, the traditional dataset partition method may cause information leakage, which poses challenges for a fair comparison between models. The performance of the model based on "convolutional-pooling-fully connected" structure is limited by small sample sizes and high dimensions of HSRSI. Moreover, most current studies did not involve how to choose the number of principal components with the application of the principal component analysis (PCA) to reduce dimensionality. To overcome the above challenges, firstly, the non-overlapping sliding window strategy combined with the judgment mechanism is introduced, used to split the hyperspectral dataset. Then, a PSE-UNet model for HSRSI semantic segmentation is designed by combining PCA, the attention mechanism, and UNet, and the factors affecting the performance of PSE-UNet are analyzed. Finally, the cumulative variance contribution rate (CVCR) is introduced as a dimensionality reduction metric of PCA to study the Hughes phenomenon. The experimental results with the Salinas dataset show that the PSE-UNet is superior to other semantic segmentation algorithms and the results can provide a reference for HSRSI semantic segmentation.

AGVS: A New Change Detection Dataset for Airport Ground Video Surveillance

Change detection is the foundation of intelligent video surveillance of the airport ground. However, experiments have shown that change detection algorithms with good performance on traditional datasets (e.g., CDnet2014) perform poorly in airport ground surveillance. The reason is that traditional datasets focus on the diversity of scenarios, while the practical application requires robustness against various changes in a single scene. We posit that the solution to this problem is to establish a unique dataset for airport ground surveillance and develop specific algorithms for this scenario. In this paper, we present an Airport Ground Video Surveillance benchmark (AGVS) for change detection of the airport ground. AGVS includes 25 long videos, amounting to about 100000 frames and accurate ground truth for all frames. Each video contains multiple challenges specific to the airport ground (e.g., haze, camouflage, strip shape, shadow and illumination change, simultaneous multi-scale objects) and various appearance changes of the aircraft). Change detection ground truth is generated by manual annotation. The AGVS benchmark can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.agvs-caac.com</uri> . Furthermore, we conduct a simple review of current change detection algorithms, both unsupervised or supervised, and then 21 state-of-the-art algorithms are tested and analyzed on the AGVS benchmark. Finally, we conclude with algorithm design principles of change detection for airport ground surveillance.

A Systematic Literature Review on Leukemia Prediction Using Machine Learning

Blood cancer is one of the most dangerous diseases in kids because it spreads throughout the body, damages healthy cells, and causes uncontrolled white blood cell growth. If it is not promptly treated, blood cancer can kill a person. Leukemia, commonly known as "White Blood Cancer," is the most common type of blood cancer and it has a devastating effect on many people. In essence, it develops in the marrow of bones. Uncontrolled white blood cell proliferation in leukemia harms the body and creates malignant cells. However, throughout the past few years, numerous computer-aided technologies have been employed to manage, identify, treat, and detect leukaemia. Many researchers presented algorithms, methods, and approaches to treat this disease, and after applying them with traditional dataset samples and some of their own created ones, they produced many positive outcomes. Because imaged data sets are utilized in medical examinations, big data plays a significant part in the data collecting for datasets. Large datasets produced positive results for machine learning algorithms such as convolutional neural networks, which are regarded as the best method for classifying images. Hematologic malignancies, which include blood-related diseases like anemia, are cancers that are connected to the blood. Leukemia and multiple myeloma, Hodgkin’s lymphoma, non-Hodgkin’s lymphoma and encompasses acute AML( Acute Myeloid leukemia ), ALL (Acute Lymphoblastic Leukemia), CML( Chronic Myeloid Leukemia ), and CLL(Chronic Lymphocytic Leukemia) and affect adolescents, young grownups and the aged, and their prevalence increases with age. The main objective of this research is to present a systematic literature review associated with survey questions on leukemia detection by using machine learning algorithms, techniques, and methods recently. In this study, we outline some past and present uses of machine learning in leukemia prediction, identify manual diagnostic issues, and then discuss the historical background of leukemia discovery. We have also covered leukemia recognition, research motivation, research limits, the composition of white blood cells, and leukemia detection techniques. In order to do our review methodically, the methodology was written with the classification of papers according to their quality.

Two-stage hybrid model for hydrological series prediction based on a new method of partitioning datasets

• A new method of dividing datasets based on PSO is proposed. • BNN incorporates Langevin dynamics in the parameter update step (LDBNN). • Integration of LDBNN, CEEMDAN, VMD and the proposed method yield the hybrid model. • The new data dividing method performs better than the traditional dividing method. • The hybrid model yields excellent point and interval predictions. Obtaining accurate point estimates and reliable interval prediction results for rainfall and runoff series is important to aid in water resource decision-making and planning management in a changing environment. In this paper, we propose a two-stage hybrid model (P-CVEE-LDBNN). The model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods for data preprocessing and a Bayesian neural network (LDBNN) based on Langevin dynamics as a prediction model. Then, a new dataset partitioning method based on a particle swarm optimization algorithm, which is different from the traditional dataset partitioning method, is applied. A one-stage hybrid model (T-CEE-LDBNN) that integrates the CEEMDAN method and the traditional method of partitioning datasets, a one-stage hybrid model (P-CEE-LDBNN) that integrates the CEEMDAN method and the new method of partitioning datasets, and the two-stage hybrid P-CVEE-LDBNN model were compared. These models were applied to monthly runoff and monthly precipitation series from seven hydrological and meteorological stations in the Yellow River basin. The mean absolute error (MAE), absolute root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (R) were used to evaluate the predictive ability of the models. The containment ratio (CR), the average bandwidth (IW), and the average asymmetry degree (S) were used to evaluate the interval prediction results of the models. The results show that (1) the subset obtained with the new method of data partitioning considering statistical properties is more favorable for model prediction. (2) The second decomposition approach based on VMD is a reliable method that can significantly improve the prediction accuracy of the final model. (3) Compared with traditional neural networks that can only obtain deterministic point prediction results, Bayesian methods can provide intervals with prediction results, making the results of the two-stage hybrid P-CVEE-LDBNN model highly reliable.

Social media for landscape planning and design: a review and discussion

This report systematically reviews the academic literature on social media’s applications in landscape planning and design. As an emerging data source, social media help overcome the limitations associated with traditional datasets that focus primarily on environmental information; they explicitly or implicitly reveal important information concerning human behaviours, landscape values, and landscape perceptions. Key findings include: (1) social media data can be valid proxies for data collected from traditional methods, while presenting advantages of cost and time savings, and capturing the intangible and subjective dimension of cultural ecosystem services; (2) geospatial location, text information, and photo content are the primary data parameters in use; and (3) most studies currently focus on large/regional-scale, nonurban areas. We further identified four themes that characterise the current stage of social media applications. Challenges and prospects of social media in landscape studies are also discussed.

Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G () with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H−1) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h2 = 0.3) were available for all animals in generations 1–9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G−1 and using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates.

Genomics outperforms genetics to manage mistakes in fisheries stocking of threatened species

Hatchery production and fisheries stocking is a widespread and high profile management practice because it allows recreational fisheries to continue in threatened species. Human-mediated transfer of fish across the geographic boundaries of intraspecies lineages or closely related species can cause introgression and occasionally outbreeding depression. Hybridization can be difficult to detect due to limited morphological differences among close lineages and the relatively low power of traditional genetic datasets. Here we showcase the use of genomic techniques to detect admixture of the economically important and threatened golden perch (Macquaria ambigua) in the Murray-Darling Basin, southeast Australia. We detected admixture through a genome-wide dataset of 6,862 single nucleotide polymorphisms (SNPs) across 174 Murray-Darling sourced fish and 15 fish from each of two neighbouring basins: the Lake Eyre and Fitzroy basins. Individuals with partial ancestry from both neighboring basins were detected using genomics throughout the Murray-Darling, suggesting the release of individuals and introgression into the Murray-Darling Basin. Importantly, a traditional microsatellite dataset was unreliable for identifying admixed individuals. The SNP-detected admixed individuals were also found in Murray-Darling impoundments, where fish are solely sourced from government-managed hatcheries, suggesting that some broodstock in hatcheries might have non-endemic ancestry. Stocking programs for golden perch release over one million fingerlings each year, and so could impact the genetic variation in the wild. We advocate for using genomics to check the ancestry of broodstock and for increasing collaboration between managers and academics—as done here—to better integrate the power of genomics into biodiversity management and conservation.

Lossless Coding of Light Fields Based on 4D Minimum Rate Predictors.

Common representations of light fields use four-dimensional data structures, where a given pixel is closely related not only to its spatial neighbours within the same view, but also to its angular neighbours, co-located in adjacent views. Such structure presents increased redundancy between pixels, when compared with regular single-view images. Then, these redundancies are exploited to obtain compressed representations of the light field, using prediction algorithms specifically tailored to estimate pixel values based on both spatial and angular references. This paper proposes new encoding schemes which take advantage of the four-dimensional light field data structures to improve the coding performance of Minimum Rate Predictors. The proposed methods expand previous research on lossless coding beyond the current state-of-the-art. The experimental results, obtained using both traditional datasets and others more challenging, show bit-rate savings no smaller than 10%, when compared with existing methods for lossless light field compression.

A Comparison Between Various Human Detectors and CNN-Based Feature Extractors for Human Activity Recognition via Aerial Captured Video Sequences

Human detection and activity recognition (HDAR) in videos plays an important role in various real-life applications. Recently, object detection methods such as &#x201C;you only look once&#x201D; (YOLO), faster region based convolutional neural network (R-CNN), and EfficientDet have been used to detect humans in videos for subsequent decision-making applications. This paper aims to address the problem of human detection in aerial captured video sequences using a moving camera attached to an aerial platform with dynamical events such as varied altitudes, illumination changes, camera jitter, and variations in viewpoints, object sizes and colors. Unlike traditional datasets that have frames captured by a static ground camera with medium or large regions of humans in these frames, the UCF-ARG aerial dataset is more challenging because it contains videos with large distances between the humans in the frames and the camera. The performance of human detection methods that have been described in the literature are often degraded when input video frames are distorted by noise, blur, illumination changes, and the like. To address these limitations, the object detection methods used in this study were trained on the COCO dataset and evaluated on the publicly available UCF-ARG dataset. The comparison between these detectors was done in terms of detection accuracy. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that EfficientDetD7 was able to outperform other detectors with 92.9% average accuracy in detecting all activities and various conditions including blurring, addition of Gaussian noise, lightening, and darkening. Additionally, deep pre-trained convolutional neural networks (CNNs) such as ResNet and EfficientNet were used to transfer learning from the ImageNet dataset to the UCF-ARG dataset and to extract highly informative features from the detected and cropped human patches. The extracted spatial features were utilized by Long Short-Term Memory (LSTM) to consider temporal relations between features for human activity recognition (HAR). Experimental results found that the EfficientNetB7-LSTM was able to outperform existing HAR methods in terms of average accuracy (80%), average precision (83%), average recall (80%), average F1 score (80%), average false negative rate (FNR) (20%), average false positive rate (FPR) (4.8%), and average Area Under Curve (AUC) (94%). The outcome is a robust HAR system which combines EfficientDetD7, and EfficientNetB7 with LSTM for human detection and activity classification, respectively.

Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

Estimating disease vector population size from citizen science data.

Citizen science projects have the potential to address hypotheses requiring extremely large datasets that cannot be collected with the financial and labour constraints of most scientific projects. Data collection by the general public could expand the scope of scientific enquiry if these data accurately capture the system under study. However, data collection inconsistencies by the untrained public may result in biased datasets that do not accurately represent the natural world. In this paper, we harness the availability of scientific and public datasets of the Lyme disease tick vector to identify and account for biases in citizen science tick collections. Estimates of tick abundance from the citizen science dataset correspond moderately with estimates from direct surveillance but exhibit consistent biases. These biases can be mitigated by including factors that may impact collector participation or effort in statistical models, which, in turn, result in more accurate estimates of tick population sizes. Accounting for collection biases within large-scale, public participation datasets could update species abundance maps and facilitate using the wealth of citizen science data to answer scientific questions at scales that are not feasible with traditional datasets.

MinION sequencing from sea ice cryoconites leads to de novo genome reconstruction from metagenomes

Genome reconstruction from metagenomes enables detailed study of individual community members, their metabolisms, and their survival strategies. Obtaining high quality metagenome-assembled genomes (MAGs) is particularly valuable in extreme environments like sea ice cryoconites, where the native consortia are recalcitrant to culture and strong astrobiology analogues. We evaluated three separate approaches for MAG generation from Allen Bay, Nunavut sea ice cryoconites—HiSeq-only, MinION-only, and hybrid (HiSeq + MinION)—where field MinION sequencing yielded a reliable metagenome. The hybrid assembly produced longer contigs, more coding sequences, and more total MAGs, revealing a microbial community dominated by Bacteroidetes. The hybrid MAGs also had the highest completeness, lowest contamination, and highest N50. A putatively novel species of Octadecabacter is among the hybrid MAGs produced, containing the genus’s only known instances of genomic potential for nitrate reduction, denitrification, sulfate reduction, and fermentation. This study shows that the inclusion of MinION reads in traditional short read datasets leads to higher quality metagenomes and MAGs for more accurate descriptions of novel microorganisms in this extreme, transient habitat and has produced the first hybrid MAGs from an extreme environment.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Methods: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

High-Throughput Statistical Interrogation of Mechanical Properties with Build Plate Location and Powder Reuse in AlSi10Mg

Additive manufacturing (AM) allows agile, rapid manufacturing of geometrically complex components that would otherwise be impossible through traditional manufacturing methods. With this maturing manufacturing technology comes the need to adopt testing methods that are commensurate with the speed of additive manufacturing and take advantage of its geometric flexibility. High-throughput tensile testing (HTT) is a technique that allows a large number of tensile bars to be tested in a short amount of time. In the present study, HTT is used to evaluate AM AlSi10Mg produced using powder bed fusion with a Renishaw AM250 machine. Three parameters were varied in this study: (1) powder reuse history, (2) location on the build plate, and (3) size of the tensile specimen. For all parameter combinations, at least 22 specimens were tested; in several cases, over 40 were tested. This large dataset, consisting of over 500 tensile tests, permits Weibull statistical analysis and provides sufficient fidelity to isolate subtle trends that would have likely been missed in smaller, traditional datasets. The observed trends are rationalized in terms of the role of porosity and surface crust on mechanical response.

Millimeter‐ to Decimeter‐Scale Surface Roughness of the Moon at the Chang'e‐4 Exploration Region

AbstractThe surface slope and roughness of the Moon have been investigated extensively over a wide baseline range except millimeter to decimeter scales. In this study, we present for the first time millimeter‐to decimeter‐scale surface slope and roughness of the Moon at China's Chang'e‐4 landing regions (∼20 m across) using the Digital Terrain Model (DTM) with a resolution of 5 mm/pixel. The bidirectional slope at the 7 mm scale can be larger than 40° with a median value of ∼10°. The root‐mean‐square (RMS) height within a window size of 125 mm varies from ∼1 mm to ∼18 mm with a median value of ∼4 mm. Both the bidirectional slope and RMS height show scale‐dependent behaviors and the parameter of scale dependence, the Hurst exponent, is ∼0.6–0.85. We also synthesized the bidirectional slope at baseline from micrometer to kilometer, showing that bidirectional slope decreases from ∼60° at micrometer to ∼1° at a kilometer. At millimeter‐scale, surface roughness is mainly controlled by small impact craters, rocks, and regolith properties. Our roughness results not only bridge the gap in understanding surface roughness from traditional topographic data sets to radar and thermal observations, but also provide valuable information about lunar regolith characteristics, and small‐scale geological processes.

Physique-Based Human Activity Recognition Using Ensemble Learning and Smartphone Sensors

Traditional methods of Human Activity Recognition (HAR) do not take into consideration physical attributes of human subjects such as height, weight, gender etc. Thus, a particular recognition model does not perform consistently for different subjects having diverse physical attributes. In this paper, we propose a novel Physique-based HAR method that takes care of this problem and provides better accuracy in comparatively less time. Raw sensor data are acquired from inbuilt accelerometer and gyroscope sensor modules of smartphones. After pre-processing of the collected data physique-based datasets were prepared, based on the similarities of subjects' physique. Analysis is done on both of these physique-based datasets and normal traditional dataset with the help of various machine learning algorithms. The work not only identifies the suitable learning algorithms for HAR but also shows that the proposed physique-based method outperforms the traditional HAR approach for both publicly available dataset and our own generated dataset, with an accuracy of 99.88%. Individual activity-wise accuracy results are also compared with several recent benchmarks to show the efficiency of the proposed Physique-based HAR method.

Qualitative Data Clustering to Detect Outliers.

Detecting outliers is a widely studied problem in many disciplines, including statistics, data mining, and machine learning. All anomaly detection activities are aimed at identifying cases of unusual behavior compared to most observations. There are many methods to deal with this issue, which are applicable depending on the size of the data set, the way it is stored, and the type of attributes and their values. Most of them focus on traditional datasets with a large number of quantitative attributes. The multitude of solutions related to detecting outliers in quantitative sets, a large and still has a small number of research solutions is a problem detecting outliers in data containing only qualitative variables. This article was designed to compare three different categorical data clustering algorithms: K- algorithm taken from MacQueen’s K- algorithm and the and algorithms. The comparison concerned the method of dividing the set into clusters and, in particular, the outliers detected by algorithms. During the research, the authors analyzed the clusters detected by the indicated algorithms, using several datasets that differ in terms of the number of objects and variables. They have conducted experiments on the parameters of the algorithms. The presented study made it possible to check whether the algorithms similarly detect outliers in the data and how much they depend on individual parameters and parameters of the set, such as the number of variables, tuples, and categories of a qualitative variable.

Clustering-Based Noise Elimination Scheme for Data Pre-Processing for Deep Learning Classifier in Fingerprint Indoor Positioning System.

Wi-Fi-based indoor positioning systems have a simple layout and a low cost, and they have gradually become popular in both academia and industry. However, due to the poor stability of Wi-Fi signals, it is difficult to accurately decide the position based on a received signal strength indicator (RSSI) by using a traditional dataset and a deep learning classifier. To overcome this difficulty, we present a clustering-based noise elimination scheme (CNES) for RSSI-based datasets. The scheme facilitates the region-based clustering of RSSIs through density-based spatial clustering of applications with noise. In this scheme, the RSSI-based dataset is preprocessed and noise samples are removed by CNES. This experiment was carried out in a dynamic environment, and we evaluated the lab simulation results of CNES using deep learning classifiers. The results showed that applying CNES to the test database to eliminate noise will increase the success probability of fingerprint location. The lab simulation results show that after using CNES, the average positioning accuracy of margin-zero (zero-meter error), margin-one (two-meter error), and margin-two (four-meter error) in the database increased by 17.78%, 7.24%, and 4.75%, respectively. We evaluated the simulation results with a real time testing experiment, where the result showed that CNES improved the average positioning accuracy to 22.43%, 9.15%, and 5.21% for margin-zero, margin-one, and margin-two error, respectively.

GA2RM: A GA-Based Action Rule Mining Method

Action Mining is a subfield of Data Mining that tries to extract actions from traditional data sets. Action Rule is a type of rule that suggests some changes in its consequent part. Extracting action rules from data has been one of the research interests in recent years. Current state-of-the-art action rule mining methods like DEAR typically take classification rules as their input; Since traditional classification methods have been designed for prediction and not for manipulation, therefore extracting action rules directly from data can result in more valuable action rules. Here, we have proposed a method to generate action rules directly from data. To tackle the problem of huge search space of action rules, a Genetic Algorithm has been devised. Different metrics have been defined for investigating the effectiveness of our proposed method and a large number of experiments have been done on real and synthetic data sets. The results show that our method can find from 20% to 10 times more interesting (in case of support and confidence) action rules in comparison with its competitors.