A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition

Human gait recognition (HGR) shows high importance in the area of video surveillance due to remote access and security threats. HGR is a technique commonly used for the identification of human style in daily life. However, many typical situations like change of clothes condition and variation in view angles degrade the system performance. Lately, different machine learning (ML) techniques have been introduced for video surveillance which gives promising results among which deep learning (DL) shows best performance in complex scenarios. In this article, an integrated framework is proposed for HGR using deep neural network and fuzzy entropy controlled skewness (FEcS) approach. The proposed technique works in two phases: In the first phase, deep convolutional neural network (DCNN) features are extracted by pre-trained CNN models (VGG19 and AlexNet) and their information is mixed by parallel fusion approach. In the second phase, entropy and skewness vectors are calculated from fused feature vector (FV) to select best subsets of features by suggested FEcS approach. The best subsets of picked features are finally fed to multiple classifiers and finest one is chosen on the basis of accuracy value. The experiments were carried out on four well-known datasets, namely, AVAMVG gait, CASIA A, B and C. The achieved accuracy of each dataset was 99.8, 99.7, 93.3 and 92.2%, respectively. Therefore, the obtained overall recognition results lead to conclude that the proposed system is very promising

Studies of the Decay B+- -> D_CP K+-

We report studies of the decay B+- -> D_CP K+-, where D_CP denotes neutral D mesons that decay to CP eigenstates. The analysis is based on a 29.1/fb data sample of collected at the \Upsilon(4S) resonance with the Belle detector at the KEKB asymmetric e+ e- storage ring. Ratios of branching fractions of Cabibbo-suppressed to Cabibbo-favored processes involving D_CP are determined to be B(B- -> D_1 K-)/B(B- -> D_1 pi-)=0.125 +- 0.036 +- 0.010 and B(B- -> D_2 K-)/B(B- -> D_2 pi-)=0.119 +- 0.028 +- 0.006, where indices 1 and 2 represent the CP=+1 and CP=-1 eigenstates of the D0 - anti D0 system, respectively. We also extract the partial rate asymmetries for B+- -> D_CP K+-, finding A_1 = 0.29 +- 0.26 +- 0.05 and A_2 = -0.22 +- 0.24 +- 0.04.Comment: 10 pages, 2 figures, submitted to Physical Review Letter

Al2O3-based nanofluids: a review

Ultrahigh performance cooling is one of the important needs of many industries. However, low thermal conductivity is a primary limitation in developing energy-efficient heat transfer fluids that are required for cooling purposes. Nanofluids are engineered by suspending nanoparticles with average sizes below 100 nm in heat transfer fluids such as water, oil, diesel, ethylene glycol, etc. Innovative heat transfer fluids are produced by suspending metallic or nonmetallic nanometer-sized solid particles. Experiments have shown that nanofluids have substantial higher thermal conductivities compared to the base fluids. These suspended nanoparticles can change the transport and thermal properties of the base fluid. As can be seen from the literature, extensive research has been carried out in alumina-water and CuO-water systems besides few reports in Cu-water-, TiO2-, zirconia-, diamond-, SiC-, Fe3O4-, Ag-, Au-, and CNT-based systems. The aim of this review is to summarize recent developments in research on the stability of nanofluids, enhancement of thermal conductivities, viscosity, and heat transfer characteristics of alumina (Al2O3)-based nanofluids. The Al2O3 nanoparticles varied in the range of 13 to 302 nm to prepare nanofluids, and the observed enhancement in the thermal conductivity is 2% to 36%

Measurement of the B-0-(B)over-bar(0) mixing parameter Delta m(d) using semileptonic B-0 decays

We present a measurement of the B-0-(B-0) over bar mixing parameter Deltam(d) using neutral B meson pairs in a 29.1 fb(-1) data sample collected at the Y(4S) resonance with the Belle detector at the KEKB asymmetric-energy e(+)e(-) collider. We exclusively reconstruct one neutral B meson in the semileptonic B-0-->D*-.(+)nu decay mode and identify the flavor of the accompanying B meson from its decay products. From the distribution of the time intervals between the two flavor-tagged B meson decay points, we obtain Deltam(d)=(0.494+/-0.012+/-0.015) ps(-1), where the first error is statistical and the second error is systematic

Observation of B-+/-->omega K-+/- decay

We report the first observation of the charmless two-body mode B+/- --> omegaK(+/-) decay, and a new measurement of the branching fraction for the B+/- --> omegapi(+/-) decay. The measured branching fractions are B(B+/- --> omegaK(+/-)) = (9.2(-2.3)(+2.6) +/-1.0) x 10(-6) and B(B+/- --> omegapi(+/-)) = (4.2(-1.8)(+2.0) +/- 0.5) x 10(-6). We also measure the partial rate asymmetry of B+/- --> omegaK(+/-) decays and obtain A(CP) = -0.21 +/- 0.28 +/- 0.03. The results are based on a data sample of 29.4 fb(-1) collected on the Y(4S) resonance by the Belle detector at the KEKB e(+)e(-) collider

Fruit category classification by fractional Fourier entropy with rotation angle vector grid and stacked sparse autoencoder

AimFruit category classification is important in factory packing and transportation, price prediction, dietary intake, and so forth.MethodsThis study proposed a novel artificial intelligence system to classify fruit categories. First, 2D fractional Fourier entropy with rotation angle vector grid was used to extract features from fruit images. Afterwards, a five-layer stacked sparse autoencoder was used as the classifier.ResultsTen runs on the test set showed our method achieved a micro-averaged F1 score of 95.08% for an 18-category fruit dataset.ConclusionOur method gives better micro-averaged F1 score than 10 state-of-the-art approaches.</div

PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches