Effective Evolutionary Multilabel Feature Selection under a Budget Constraint

Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multilabel classification with a budget constraint. The proposed method employs a novel exploration operation to enhance the search capabilities of a traditional genetic search, resulting in improved multilabel classification. Empirical studies using 20 real-world datasets demonstrate that the proposed method outperforms conventional multilabel feature selection methods

Performance Comparison of CRUD Operations in IoT based Big Data Computing

Nowadays, due to the development of mobile devices, the kinds of data that are generated are becoming diverse, and the amount is becoming huge. The vast amount of data generated in this way is called big data. Big data must be processed in a different way than existing data processing methods. Representative methods of big data processing are RDBMS (Relational Database System) and NoSQL method. We compare NoSQL and RDBMS, which are representative database systems. In this paper, we use MySQL query and MongoDB query to compare RDBMS and NoSQL. We gradually compare the performance of CRUD operations in MySQL and MongoDB by increasing the amount of data. MongoDB sets index and compares it again.  Through result of these operations is to choose a database system that fits the situation.  This makes it possible to design and analyse big data more efficiently.

Detecting Variability in Massive Astronomical Time-series Data. II. Variable Candidates in the Northern Sky Variability Survey

We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method, which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves having data points at more than 15 epochs as variable and non-variable candidates in 638 NSVS fields. Variable candidates are selected depending on how strongly they are separated from the largest cluster and how rarely they are grouped together in eight-dimensional space spanned by variability indices. All NSVS light curves are also cross-correlated with IRAS , AKARI, Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and GALEX objects, as well as known objects in the SIMBAD database. The variability analysis and cross-correlation results are provided in a public online database, which can be used to select interesting objects for further investigation. Adopting conservative selection criteria for variable candidates, we find about 1.8 million light curves as possible variable candidates in the NSVS data, corresponding to about 10% of our entire NSVS sample. Multi-wavelength colors help us find specific types of variability among the variable candidates. Moreover, we also use morphological classification from other surveys such as SDSS to suppress spurious cases caused by blending objects or extended sources due to the low angular resolution of the NSVS.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98631/1/1538-3881_143_3_65.pd

The EPOCH Project: I. Periodic variable stars in the EROS-2 LMC database

The EPOCH (EROS-2 periodic variable star classification using machine learning) project aims to detect periodic variable stars in the EROS-2 light curve database. In this paper, we present the first result of the classification of periodic variable stars in the EROS-2 LMC database. To classify these variables, we first built a training set by compiling known variables in the Large Magellanic Cloud area from the OGLE and MACHO surveys. We crossmatched these variables with the EROS-2 sources and extracted 22 variability features from 28 392 light curves of the corresponding EROS-2 sources. We then used the random forest method to classify the EROS-2 sources in the training set. We designed the model to separate not only $\delta$ Scuti stars, RR Lyraes, Cepheids, eclipsing binaries, and long-period variables, the superclasses, but also their subclasses, such as RRab, RRc, RRd, and RRe for RR Lyraes, and similarly for the other variable types. The model trained using only the superclasses shows 99% recall and precision, while the model trained on all subclasses shows 87% recall and precision. We applied the trained model to the entire EROS-2 LMC database, which contains about 29 million sources, and found 117 234 periodic variable candidates. Out of these 117 234 periodic variables, 55 285 have not been discovered by either OGLE or MACHO variability studies. This set comprises 1 906 $\delta$ Scuti stars, 6 607 RR Lyraes, 638 Cepheids, 178 Type II Cepheids, 34 562 eclipsing binaries, and 11 394 long-period variables. A catalog of these EROS-2 LMC periodic variable stars will be available online at http://stardb.yonsei.ac.kr and at the CDS website (http://vizier.u-strasbg.fr/viz-bin/VizieR).Comment: 18 pages, 20 figures, suggseted language-editing by the A&A editorial office is applie

SREBP and MDT-15 protect C. elegans from glucose-induced accelerated aging by preventing accumulation of saturated fat

Glucose-rich diets shorten the life spans of various organisms. However, the metabolic processes involved in this phenomenon remain unknown. Here, we show that sterol regulatory element-binding protein (SREBP) and mediator-15 (MDT-15) prevent the life-shortening effects of a glucose-rich diet by regulating fat-converting processes in Caenorhabditis elegans. Up-regulation of the SREBP/MDT-15 transcription factor complex was necessary and sufficient for alleviating the life-shortening effect of a glucose-rich diet. Glucose feeding induced key enzymes that convert saturated fatty acids (SFAs) to unsaturated fatty acids (UFAs), which are regulated by SREBP and MDT-15. Furthermore, SREBP/MDT-15 reduced the levels of SFAs and moderated glucose toxicity on life span. Our study may help to develop strategies against elevated blood glucose and free fatty acids, which cause glucolipotoxicity in diabetic patients.112217Ysciescopu

Mediastinal lymphoma in a young Turkish Angora cat

An 8-month old intact male Turkish Angora cat was referred to the Veterinary Medical Teaching Hospital (VMTH), Seoul National University, for an evaluation of anorexia and severe dyspnea. The thoracic radiographs revealed significant pleural effusion. A cytology evaluation of the pleural fluid strongly suggested a lymphoma containing variable sized lymphocytes with frequent mitotic figures and prominent nucleoli. The feline leukemia virus and feline immunodeficiency virus tests were negative. The cat was euthanized at his owner's request and a necropsy was performed. A mass was detected on the mediastinum and lung lobes. A histopathology evaluation confirmed the mass to be a lymphoma. Immunohistochemistry revealed the mass to be CD3 positive. In conclusion, the cat was diagnosed as a T-cell mediastinal lymphoma

Resection of individually identified high-rate high-frequency oscillations region is associated with favorable outcome in neocortical epilepsy

Objectives: High-frequency oscillations (HFOs) represent a novel electrophysiologic marker of endogenous epileptogenicity. Clinically, this propensity can be utilized to more accurately delineate the resection margin before epilepsy surgery. Currently, prospective application of HFOs is limited because of a lack of an exact quantitative measure to reliably identify HFO-generating areas necessary to include in the resection. Here, we evaluated the potential of a patient-individualized approach of identifying high-rate HFO regions to plan the neocortical resection. Methods: Fifteen patients with neocortical seizure-onset zones (SOZs) underwent intracranial electroencephalographic monitoring. To identify interictal HFOs, we applied an automated, hypersensitive HFO-detection algorithm followed by post hoc processing steps to reject false detections. The spatial relationship between HFO distribution and the SOZ was evaluated. To address high interpatient variability in HFO properties, we evaluated the high-rate HFO region, an unbiased statistical parameter, in each patient. The relationship between resection of the high-rate HFO region and postoperative outcome was examined. Results: Grouped data demonstrated that the rate of ripple (60–200 Hz) and fast ripple (200–500 Hz) was increased in the SOZ (both p < 0.01). Intrapatient analysis of the HFO distribution localized the SOZ in 11 patients. High-rate HFO regions were determined in all patients by an individually adjusted threshold. Resection of high-rate HFO regions was significantly associated with a seizure-free outcome (p < 0.01). The extent/ratio of SOZ or spiking region resection did not differ between seizure-free and seizure-persistent groups. Significance: Intrapatient analysis of high-rate HFOs provides more detailed description of HFO-generating areas and can mark the areas of clinically significant epileptogenicity—a crucial component of the neocortical epileptic network that should be removed to achieve a good outcome. Validating and adopting an unbiased quantitative HFO parameter has the potential to propel wider and prospective utilization of HFOs in the surgical treatment of neocortical epilepsy and to improve its outcome