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Pseudomonas aeruginosa (JQ989348) was isolated from deep sea water sample and used for synthesis of silver nanoparticles (AgNPs). AgNPs were confirmed by analyzing surface plasmon resonance using UV-visible spectrophotometer at 420 nm. Further scanning electron microscope analysis confirmed the range of particle size between 13 and 76 nm and XRD pattern authorizes the anisotropic crystalline nature of AgNPs. Fourier transform infrared spectrum endorsed the presence of high amount of proteins and other secondary metabolites in synthesized AgNPs influence the reduction process and stabilization of nanoparticles. The inhibitory activity of AgNPs was tested against human pathogens showed high activity against Eschericia coli, Vibrio cholerae, Aeromonas sp., and Cornebacterium sp. demonstrating its antimicrobial value against pathogenic diseases. Additionally, biologically synthesized AgNPs have notable anti-biofilm activity against primary biofilm forming bacteria P. aeruginosa and Staphylococcus aureus. The MTT assay method was evaluated using human cervical cancer cells exposed the AgNPs have excellent cytotoxic activity. [ABSTRACT FROM AUTHOR]

This paper presents a method to classify audio-video data into one of five classes: advertisement, cartoon, news, movie and songs. Automatic audio-video classification is very useful to audio-video indexing and content based audio-video retrieval. Mel Frequency Cepstral Coefficients (MFCC) are used to characterize the audio data. The color histogram features extracted from the images in the video clips are used as visual features. Support Vector Machine (SVM) is used for audio and video classification. The experiments on different genres illustrate that the results of classification are significant and effective. Experimental results of audio classification and video classification are combined using weighted sum rule for audio-video based classification. The method classifies the audio-video clips with an accuracy of 87.0%. [ABSTRACT FROM AUTHOR]

Psychological distress and disabilities are increasingly identified among general population. Psychiatrist availability in rural areas is poor and often general practitioners have to identify and treat psychiatric problems like depression and anxiety. This paper proposes a method to identify the psychiatric problems among patients using multimodel decision support system. Backpropagation (BP) and Radial Basis Function (RBF) neural network models are used to design the decision support system. Forty four factors are considered for feature extraction. The features are collected from 400 patients and divided into four sets of equal size. Three sets of patient features are used to train the decision support system and one set of patient features is used to evaluate performance of the system. The experimental results showed that the proposed method achieves an accuracy of 98.75% for identifying the psychiatric problems. [ABSTRACT FROM AUTHOR]

Audio classification has been a focus area in the research of audio processing and pattern recognition. Automatic audio classification is very useful to audio indexing, content-based audio retrieval and online audio distribution, but the extraction of the most common and salient themes from unstructured raw audio data is a major challenge. The paper presents effective algorithms to automatically classify audio clips into one of the six classes: music, news, sports, advertisement, cartoon and movie. For these categories, a number of acoustic features that include linear predictive coefficients (LPC), linear predictive cepstral coefficients (LPCC) and Mel frequency cepstral coefficients (MFCC) are extracted to characterize the audio content. The auto associative neural network model (AANN) is used to capture the distribution of the acoustic feature vectors. The AANN model captures the distribution of the acoustic features of a class, and the back propagation learning algorithm is used to adjust the weights of the network to minimize the mean square error for each feature vector. This work also proposes an efficient audio indexing system which indexes movie clips using K-means clustering algorithm. Experimental results indicate that the proposed algorithms can produce satisfactory results. [ABSTRACT FROM AUTHOR]

This paper proposes an unsupervised method for improving the automatic speaker segmentation performance by combining the evidence from residual phase (RP) and mel frequency cepstral coefficients (MFCC). This method demonstrates the complementary nature of speaker specific information present in the residual phase in comparison with the information present in the conventional MFCC. Moreover this method presents an unsupervised speaker segmentation algorithm based on support vector machine (SVM). The experiments show that the combination of residual phase and MFCC helps to identify more robustly the transitions among speakers. [Copyright &y& Elsevier]

This paper proposes a facial and visual speech feature extraction method for automatic person authentication in video. The method proposed in Viola and Jones (2006) is used to detect the face region. Face region is processed in YCbCr color space to determine the locations of the eyes. The system models the non-lip region of the face using a Gaussian distribution, and it is used to locate the center of the mouth. Facial and visual speech features are extracted using multiscale morphological erosion and dilation operations, respectively. The facial features are extracted relative to the locations of the eyes and visual speech features are extracted relative to the locations of the eyes and mouth. Auto-Associative Neural Network (AANN) and Support Vector Machines (SVMs) are analyzed for person authentication. AANN models are used to capture the distribution of facial and visual speech features of a subject. SVMs are used to construct the optimal separating hyperplane for facial and visual speech features. The evidence from face and visual speech modalities are combined using a weighting rule, and the result is used to accept or reject the identity claim of the subject. The performance of the system is evaluated for XM2VTS database. It is seen that the system achieves an Equal Error Rate (EER) of about 0.41% and 0.37% for 50 subjects using AANN and SVM, respectively. Finally, the performance of the AANN and SVM models for person authentication are compared. Experimental results show that the SVM gives better performance than the AANN model. [ABSTRACT FROM AUTHOR]

Face detection, feature extraction and modeling are the major issues in tracking faces in a video that can be applied for facial expression recognition, video conferencing, video surveillance, etc. In this paper, a method is proposed for tracking faces in a video. A face in a video sequence is detected by a feature based system using a boosted cascade of simple features [1]. A rectangular bounding box is fitted over the detected face region using those features. The face and nonface regions are modeled using Support Vector Machines (SVM) and Radial Basis Function (RBF) network. These models are used to track the face region in the video sequence. The feature vectors are extracted by placing rectangular grid of different sizes like 3 x 3, 5 x 5 and 8 x 8 pixels over face and nonface regions. The performance of the proposed method is evaluated using real time video sequences. Experimental results show that the SVM and RBF network track the face about 82% and 93% of the frames respectively for the rectangular grid of size 8 x 8. [ABSTRACT FROM AUTHOR]

Abstract: The present study is designed to evaluate the antimicrobial, antioxidant and anticancer activities of Rhizophora apiculata. Initially, the phenolic and flavonoid content was quantified in solvent extracts, and gallic acid and rutin were used as a control, respectively. Further, antimicrobial and minimal inhibitory activities of different solvent extracts were assessed against human clinical pathogenic bacteria, and the results showed that butanol and methanol extract has potential antimicrobial activity. FTIR analysis of solvent extracts showed the presence of phenolic compounds at 3409-3430 cm−1 that actively involved in various applications including antioxidant and anticancer activities. The in vitro antioxidant activity of solvent extracts showed excellent antioxidant potential, about 84% of DPPH free-radical scavenging, 76% of hydrogen peroxide, 82% of hydroxyl radical scavenging, and 80% of reducing power. Two-way ANOVA analysis showed that the highly significant effect of antioxidant activity depends on the concentration of extracts. The DNA protection efficiency of extracts against oxidative damage was confirmed by DNA nicking assay using bacterial DNA. The methanol extract effectively inhibited the growth and induces the apoptosis through ROS generation and sensitizes the mitochondrial membrane potential of A549 lung cancer cells. Taken together, the results showed that the solvent extracts of R. apiculata could be potential antioxidant and anticancer agents. [ABSTRACT FROM AUTHOR]

Abstract: Psychological distress and disabilities are increasingly identified among general population. Psychiatrist availability in rural areas is poor and often general practitioners have to identify and treat psychiatric problems like depression and anxiety. This work proposes a method to identify the psychiatric problems among patients using multimodel decision support system. Backpropagation neural networks (BPNN), radial basis function neural network (RBFNN) and support vector machine (SVM) models are used to design the decision support system. Forty-four factors are considered for feature extraction. The features are collected from 400 patients and divided into four sets of equal size. Three sets of patient features are used to train the decision support system and one set of patient feature are used to evaluate performance of the system. Experimental results show that the proposed method achieves an accuracy of 98.75% for identifying the psychiatric problems. [ABSTRACT FROM AUTHOR]