A method for automatic classification of gender based on text- independent handwriting

Handwriting recognition is used for the prediction of various demographic traits such as age, gender, nationality, etc. Out of all the applications gender prediction is mainly admired topic among researchers. The relation between gender and handwriting can be seen from the physical appearance of the handwriting. This research work predicts gender from handwriting using the landmarks of differences between the two genders. We use the shape or visual appearance of the handwriting for extracting features of the handwriting such as slanteness (direction), area (no of pixels occupied by text), perimeter (length of edges), etc. Classification is carried out using the Support Vector Machine (SVM) as a classifier which transforms the nonlinear problem into linear using its kernel trick, logistic regression, KNN and at the end to enhance the classification rates we use Majority Voting. The experimental results obtained on a dataset of 282 writers with 2 samples per writer shows that the proposed method attains appealing performance on writer detection and text-independent environment.

     

Co-processing as a tool to improve aqueous dispersibility of cellulose ethers

Cellulose ethers are important materials with numerous applications in pharmaceutical industry. They are widely employed as stabilizers and viscosity enhancers for dispersed systems, binders in granulation process and as film formers for tablets. These polymers, however, exhibit challenge during preparation of their aqueous dispersions. Rapid hydration of their surfaces causes formation of a gel that prevents water from reaching the inner core of the particle. Moreover, the surfaces of these particles become sticky, thus leading to agglomeration, eventually reducing their dispersion kinetics. Numerous procedures have been tested to improve dispersibility of cellulose ethers. These include the use of cross-linking agents, alteration in the synthesis process, adjustment of water content of cellulose ether, modification by attaching hydrophobic substituents and co-processing using various excipients. Among these, co-processing has provided the most encouraging results. This review focuses on the molecular mechanisms responsible for the poor dispersibility of cellulose ethers and the role of co-processing technologies in overcoming the challenge. An attempt has been made to highlight various co-processing techniques and specific role of excipients used for co-processing.     

Effect of Dopants and Sintering Method on the Properties of Ceria-Based Electrolytes for IT-SOFCs Applications

Doped and co-doped ceria ceramics are used as electrolyte materials in solid oxide fuel cells. In this work, ceria-based oxides, Ce_0.90Gd_0.06Y_0.02M_0.02O_2?δ (M?=?Ca, Fe, La, and Sr) were prepared by conventional as well as microwave processing from the precursors prepared by the mixed oxide method. The consolidated calcined powders in pellet form were sintered in microwave energy at 1400°C for 20 min and in an electric furnace of IR radiation at 1400°C for 6 h. The x-ray diffraction analysis confirmed that all the compositions were crystallized into a cubic fluorite structure. Surface morphology of the sintered products was studied using scanning electron microscopy and the microhardness was investigated using the Vickers hardness test. The comparative results analysis shows that the microwave-sintered samples have uniform grain growth, higher density and higher microhardness than the corresponding conventionally sintered products. The microwave-sintered sample of composition Ce_0.90Gd_0.06Y_0.02Sr_0.02O_2?δ was found to have the highest microhardness among the four compositions due to its high density and smallest grain size.     

Physicochemical investigations of Pd2+ substituted ZnO nanoflowers for liquefied petroleum gas sensing

Journal of Materials Science: Materials in Electronics - Present study portrays, physicochemical investigations of pristine and Pd2+ modified ZnO nanoflowers (NFs) compositional series...     

Combining Bayesian optimization and Lipschitz optimization

Machine Learning - Bayesian optimization and Lipschitz optimization have developed alternative techniques for optimizing black-box functions. They each exploit a different form of prior about the...     

Principal components null space analysis for image and video classification.

We present a new classification algorithm, principal component null space analysis (PCNSA), which is designed for classification problems like object recognition where different classes have unequal and nonwhite noise covariance matrices. PCNSA first obtains a principal components subspace (PCA space) for the entire data. In this PCA space, it finds for each class "i," an Mi-dimensional subspace along which the class' intraclass variance is the smallest. We call this subspace an approximate null space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class' mean in the class' ANS is a minimum. We derive upper bounds on classification error probability of PCNSA and use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA). We propose a practical modification of PCNSA called progressive-PCNSA that also detects "new" (untrained classes). Finally, we provide an experimental comparison of PCNSA and progressive PCNSA with SLDA and PCA and also with other classification algorithms-linear SVMs, kernel PCA, kernel discriminant analysis, and kernel SLDA, for object recognition and face recognition under large pose/expression variation. We also show applications of PCNSA to two classification problems in video--an action retrieval problem and abnormal activity detection.     

Proteome profiling of exosomes derived from plasma of heifers with divergent genetic merit for fertility

The current study evaluated exosomes isolated from plasma of heifers bred to have high or low fertility through developing extreme diversity in fertility breeding values, however, key animal traits (e.g., body weight, milk production, and percentage of North American genetics) remained similar between the 2 groups. The exosomes were isolated by a combined ultracentrifugation and size exclusion chromatography approach and characterized by their size distribution (nanoparticle tracking analysis), morphology (transmission electron microscopy), and presence of exosomal markers (immunoblotting). In addition, a targeted mass spectrometry approach was used to confirm the presence of 2 exosomal markers, tumor susceptibility gene 101 and flotillin 1. The number of exosomes from plasma of high fertility heifers was greater compared with low fertility heifers. Interestingly, the exosomal proteomic profile, evaluated using mass spectrometry, identified 89 and 116 proteins in the high and low fertility heifers respectively, of which 4 and 31 were unique, respectively. These include proteins associated with specific biological processes and molecular functions of fertility. Most notably, the tetratricopeptide repeat protein 41-related, glycodelin, and kelch-like protein 8 were identified in plasma exosomes unique to the low fertility heifers. These proteins are suggested to play a role in reproduction; however, the role of these proteins in dairy cow reproduction remains to be elucidated. Their identification underscores the potential for proteins within exosomes to provide information on the fertility status and physiological condition of the cow. This may potentially lead to the development of prognostic tools and interventions to improving dairy cow fertility.     

Investigation of magnetic field-assisted EDM of composites

The Electrical Discharge Machining (EDM) technique was performed under the magnetic field influence to determine the material removal mechanism as well as surface roughness (SR) of nonmagnetic material. This study presents an exploration of the hybrid EDM technique assisted by magnetic field, with an aim to improve process performance. Herein, magnetic field intensity, peak current, duration of pulse-on/off, tool electrode material, and SiC percentage distribution were opted as the machining parameters. The chosen parameters were analyzed for their effects on the material removal rate (MRR) and SR while machining of SiC-reinforced aluminum-based metal matrix composites. Taguchi methodology was adopted for optimization of process parameters to achieve better MRR and lower SR. The experimental results witnessed improved surface finish and enhanced material removal ability of the process and also inferred that the magnetic field-assisted EDM facilitated the process stability.