Forecasting Bitcoin Price Using Interval Graph and ANN Model: A Novel Approach.

The accurate prediction of the Bitcoin price can provide decision support for investors and a reference for governments to make regulatory policies. The Bitcoin price prediction requires a careful analysis and representation due to its data characteristics such as highly volatile, highly non-linear, non-stationary, non-linear dynamics, no periodicity, and existence of spectrum of scaling components, noisy data, and randomness. The price can be effectively forecasted by transforming the original data into another amenable form along with AI tools. In this paper, we used Interval Graph (IG) for transforming original data which is amenable for applying Artificial Neural Networks (ANN) model to predict Bitcoin price. The Bitcoin price, which is a time-series data, is captured in the form of windows representing price of day, week, and month, respectively. We have used three evaluation metrics, such as MAPE, RMSE, and Dstat. The empirical study has clearly demonstrated the encouraging performance and effectiveness of the IG-ANN. The performance is compared with traditional ANN techniques on bitcoin time-series data spanning 2013-2019 and found that IG-ANN is outperforming all.

Geometrical features of lips using the properties of parabola for recognizing facial expression.

Various real-time applications such as Human-Computer Interactions, Psychometric analysis, etc. use facial expressions as one of the important parameters. The researchers have used Action Units (AU) of the face as feature points and its deformation is compared with the reference points on the face to estimate the facial expressions. Among many parts of the face, features from the mouth contribute largely to all the well-known emotions. In this paper, the parabola theory is used to identify and mark various points on the lips. These points are considered as feature points to construct feature vectors. The Latus Rectum, Focal Point, Directrix, Vertex, etc. are also considered to identify the feature points of the lower lips and upper lips. The proposed approach is evaluated on benchmark datasets such as JAFFEE and Cohn-Kanade dataset and it is found that the performance is encouraging in understanding the facial expressions. The results are compared with contemporary methods and found that the proposed approach has given good classification accuracy in recognizing facial expressions.

3-[(E)-2-(2-Meth-oxy-phen-yl)vin-yl]-5,5-di-methyl-cyclo-hex-2-enone.

The title compound, C17H20O2, has an E conformation about the bridging C=C bond. The cyclo-hexene ring adopts an envelope conformation with the dimethyl-substituted C atom as the flap. Its mean plane makes a dihedral angle of 7.20 (12)° with the benzene ring. In the crystal, neighbouring mol-ecules are connected via C-H⋯O hydrogen bonds, forming chains running along the a-axis direction.

3-[(E)-2-(4-Chloro-phen-yl)ethen-yl]-5,5-di-methyl-cyclo-hex-2-en-1-one.

In the title compound, C16H17ClO, the cyclo-hexene ring adopts a half-chair conformation and the best plane through the six ring atoms makes a dihedral angle of 6.69 (7)° with the chlorophenyl ring. In the crystal, pairs of C-H⋯O hydrogen bonds link the mol-ecules into centrosymmetric R 2 (2)(20) dimers. The dimers are linked into an infinite chains along the b-axis direction by further C-H⋯O hydrogen bonds.

A fuzzy rule-based approach for characterization of mammogram masses into BI-RADS shape categories.

We present new geometric shape and margin features for classifying mammogram mass lesions into BI-RADS shape categories: round, oval, lobular and irregular. According to Breast Imaging Reporting and Data System (BIRADS), masses can be differentiated using its shape, size and density, which is how radiologist visualizes the mammograms. Measuring regular and irregular shapes mathematically is found to be a difficult task, since there is no single measure available to differentiate various shapes. It is known that for mammograms, shape features are superior to Haralick and wavelet based features. Various geometrical shape and margin features have been introduced based on maximum and minimum radius of mass to classify the morphology of masses. These geometric features are found to be good in discriminating regular shapes from irregular shapes. In this paper, each mass is described by shape feature vector consists of 17 shape and margin properties. The masses are classified into 4 categories such as round, oval, lobular and irregular. Classifying masses into 4 categories is a very difficult task compared to classifying masses as benign, malignant or normal vs. abnormal. Only shape and margin characteristics can be used to discriminate these 4 categories effectively. Experiments have been conducted on mammogram images from the Digital Database for Screening Mammography (DDSM) and classified using C5.0 decision tree classifier. Total of 224 DDSM mammogram masses are considered for experiment. The C5.0 decision tree algorithm is used to generate simple rules, which can be easily implemented and used in fuzzy inference system as if…then…else statements. The rules are used to construct the generalized fuzzy membership function for classifying the masses as round, oval, lobular or irregular. Proposed approach is twice effective than existing Beamlet based features for classifying the mass as round, oval, lobular or irregular.

Angiotensin converting enzyme 2 abrogates bleomycin-induced lung injury.

Despite substantial progress, mortality and morbidity of the acute respiratory distress syndrome (ARDS), a severe form of acute lung injury (ALI), remain unacceptably high. There is no effective treatment for ARDS/ALI. The renin-angiotensin system (RAS) through Angiotensin-converting enzyme (ACE)-generated Angiotensin II contributes to lung injury. ACE2, a recently discovered ACE homologue, acts as a negative regulator of the RAS and counterbalances the function of ACE. We hypothesized that ACE2 prevents Bleomycin (BLM)-induced lung injury. Fourteen to 16-week-old ACE2 knockout mice-male (ACE2(-/y)) and female (ACE2(-/-))-and age-matched wild-type (WT) male mice received intratracheal BLM (1.5U/kg). Male ACE2(-/y) BLM injured mice exhibited poorer exercise capacity, worse lung function and exacerbated lung fibrosis and collagen deposition compared with WT. These changes were associated with increased expression of the profibrotic genes α-smooth muscle actin (α-SMA) and Transforming Growth Factor ß1. Compared with ACE2(-/y) exposed to BLM, ACE2(-/-) exhibited better lung function and architecture and decreased collagen deposition. Treatment with intraperitoneal recombinant human (rh) ACE2 (2 mg/kg) for 21 days improved survival, exercise capacity, and lung function and decreased lung inflammation and fibrosis in male BLM-WT mice. Female BLM WT mice had mild fibrosis and displayed a possible compensatory upregulation of the AT2 receptor. We conclude that ACE2 gene deletion worsens BLM-induced lung injury and more so in males than females. Conversely, ACE2 protects against BLM-induced fibrosis. rhACE2 may have therapeutic potential to attenuate respiratory morbidity in ALI/ARDS.