A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision tree, and the random forest were used to create the classifier models that will help to predict breast cancer. Two different experiments were conducted using three datasets: Gene expression (GE), deoxyribonucleic acid (DNA) methylation, and a combination of the two. Six measures were used to evaluate the performance of the proposed algorithm, which include the following: Accuracy, precision, recall, specificity, area under the curve (AUC), and execution time. The effectiveness of the proposed classifiers was evaluated through comprehensive experiments. The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time. High accuracy values of 99.77%, 99.45%, and 99.45% have been achieved by SA-SVM for GE, DNA methylation, and the combined datasets, respectively. The execution time of the proposed approach was significantly reduced, in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM, which was 0.02, 0.03, and 0.02 on GE, DNA methylation, and the combined datasets respectively. In regard to precision and specificity, SA-RF obtained the best result of 100 on GE dataset. While SA-SVM attained the best recall result of 100 on GE dataset.     

Natural Convection of Nanofluid in Cylindrical Enclosure Filled with Porous Media

A numerical study has been carried out to investigate the effect of aspect ratio on heat transfer by natural convection of nanofluid taking Cu nano particles and the water as based fluid. The flow is laminar, steady state, axisymmetric two-dimensional in a vertical cylindrical channel filled with porous media. Heat is generated uniformly along the center of the channel with its vertical surface remain with cooled constant wall temperature and insulated horizontal top and bottom surfaces. The governing equations which used are continuity, momentum and energy equations using Darcy law and Boussinesq＇s approximation which are transformed to dimensionless equations. The finite difference approach is used to obtain all the computational results using the MATLAB-7 program. The parameters affected on the system are Rayleigh number ranging within （10≤ Ra ≤ 103）, aspect ratio （1 ≤ As 〈 5） and the volume fraction （0 ≤0 〈 0.2）. The results obtained are presented graphically in the form of streamline and isotherm contour plots and the results show that as ~ increase from 0.01 to 0.2 the value of the mean Nusselt number increase 50.4% for Ra = 1,000.     

Comparison of Conventional and Cross-Layer Multimedia Transport Schemes for Wireless Networks

Three competing schemes have been proposed for multimedia transport over broadband wireless channels: (a) traditional UDP (Postel, The User Datagram Protocol, 1980 [1]), (b) semi-cross-layer UDP-Lite (The Lightweight User Datagram Protocol, 2004 [2]), and (c) cross-layer header estimation (Khayam et al., IEEE Transactions on Multimedia 9(2):377–385, 2007 [3]; Khayam and Radha, IEEE Transactions on Wireless Communications 6(11):3946–3954, 2007 [4]). In all these schemes, corrupted and lost packets are recovered using FEC at the application layer. In this paper, we analytically and experimentally compare the performances of these broadband wireless multimedia schemes. First, we derive lower bounds on the excepted FEC redundancy required by ideal cross-layer header estimation, UDP and UDP-Lite over an arbitrary-order Markov wireless channel. We show that under realistic wireless channel conditions, the cross-layer header estimation scheme always requires lesser redundancy than UDP and UDP-Lite. We then propose a practical minimum distance decoding (MDD) header estimation scheme, which is receiver-based, low complexity and highly accurate. Trace-driven multimedia experiments over wireless LANs demonstrate that MDD header estimation requires significantly lesser FEC redundancy and renders better video quality than existing schemes.     

Header Detection to Improve Multimedia Quality Over Wireless Networks

Wireless multimedia studies have revealed that forward error correction (FEC) on corrupted packets yields better bandwidth utilization and lower delay than retransmissions. To facilitate FEC-based recovery, corrupted packets should not be dropped so that maximum number of packets is relayed to a wireless receiver's FEC decoder. Previous studies proposed to mitigate wireless packet drops by a partial checksum that ignored payload errors. Such schemes require modifications to both transmitters and receivers, and incur packet-losses due to header errors. In this paper, we introduce a receiver-based scheme which uses the history of active multimedia sessions to detect transmitted values of corrupted packet headers, thereby improving wireless multimedia throughput. Header detection is posed as the decision-theoretic problem of multihypothesis detection of known parameters in noise. Performance of the proposed scheme is evaluated using trace-driven video simulations on an 802.11b local area network. We show that header detection with application layer FEC provides significant throughput and video quality improvements over the conventional UDP/IP/802.11 protocol stack     

Bond Slip Analysis of Fiber-Reinforced Polymer-Strengthened Beams

The second order differential equation of interface shear is formulated for fiber-reinforced polymer-strengthened beams using beam theory with a shear deformable adhesive layer. The solution of the boundary value problem is obtained in closed form and is used to derive deflection expressions for different loading conditions. The solution is also extended to analyze partially plated beams. The results converge to the extreme cases of very poorly and perfectly bonded plates and they help identify values of the adhesive shear modulus for effective stiffening. Furthermore, the solution of partially plated beams aids in defining anchorage lengths needed to develop the full or the highest possible composite action at midspan.     

Proposition of new computer artificial intelligence models for shear strength prediction of reinforced concrete beams

Engineering with Computers - The study is investigated the capacity of new artificial intelligence (AI) methodologies for shear strength (Vs) computation of reinforced concrete (RC) beams. The...     

A Distortion-Sensitive Seam Carving Algorithm for Content-Aware Image Resizing

As displays become less expensive and are incorporated into more and more devices, there has been an increased focus on image resizing techniques to fill an image to an arbitrary screen size. Traditional methods such as cropping or resampling can introduce undesirable losses in information or distortion in perception. Recently, content-aware image retargeting methods have been proposed (Avidan and Shamir, ACM Trans Graphics 26(3), 2007; Guo et al., IEEE Trans Multimedia 11(5):856–867, 2009; Shamir and Avidan, Commun ACM 52(1), 2009; Simakov et al. 2008; Wolf et al. 2007), which produce exceptional results. In particular, seam carving, proposed by Avidan and Shamir, has gained attention as an effective solution. However, there are many cases where it can fail. In this paper we propose a distortion-sensitive seam carving algorithm for content-aware image resizing that improves edge preservation and decreases aliasing artifacts. In the proposed approach, we use local gradient information along with a thresholding technique to guide the seam selection process and provide a mechanism to halt seam carving when further processing would introduce unacceptable visual distortion in the resized image. Furthermore, anti-aliasing filter is used to reduce the aliasing artifacts caused by seam removal. Experiments have demonstrated superior performance over the current seam carving methods.     

Survey of data-selection methods in statistical machine translation

Statistical machine translation has seen significant improvements in quality over the past several years. The single biggest factor in this improvement has been the accumulation of ever larger stores of data. We now find ourselves, however, the victims of our own success, in that it has become increasingly difficult to train on such large sets of data, due to limitations in memory, processing power, and ultimately, speed (i.e. data-to-models takes an inordinate amount of time). Moreover, the training data has a wide quality spectrum. A variety of methods for data cleaning and data selection have been developed to address these issues. Each of these methods employs a search or filtering algorithm to select a subset of the data, given a defined set of feature functions. In this paper we provide a comparative overview of research in this area based on application scenario, feature functions and search method.     

Co-immobilization of chitosan and dermatan sulfate from Raja montagui skin on polyethylene terephthalate surfaces: Characterization and antibiofilm activity

In this work, polyethylene terephthalate (PET) was chemically modified by covalent co-immobilization of two separate layers of bioactive polymers composed of chitosan and a new dermatan sulfate isolated from Raja montagui skin. The new materials obtained were characterized by determination of carboxyl groups, ATR-FTIR spectroscopy, drop water contact angle, SEM, and TGA measurements. The different immobilizations modified the surface characteristics; especially when the first layer was dermatan sulfate (PET–DS–Chito) which presented the best surface wettability (71?±?3°). It also reduced the ability of Staphylococcus epidermidis to form biofilm on PET surfaces which could decrease the virulence of vascular prosthesis infections.     

A new correlation for vapor pressure prediction of natural gas mixture

In this study, the Antoine equation of vapor pressure prediction for pure substances has been extended to predict vapor pressure values of a natural gas mixture at a temperature range of 125–230 K. The correlation was modified by applying the software MATLAB 8.2 R2013b (The MathWorks, Natick, MA). The developed correlation for multicomponent natural gas mixture is predictable with few input data of critical temperature, critical pressure, and the acentric factor. For the examined natural gas mixture, the predicted vapor pressures show comparable values with those obtained by applying well-known and accurate cubical equation of state (EOS) of Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR) for the whole range of studied temperatures. From the results, an average absolute deviation of 1.735% for the developed correlation in this work was obtained from the actual results calculated experimentally, which is lower than that obtained by SRK EOS and close to results of PR EOS.