On improving the generalization of SVM classifier

The generalization performance of the SVM classifier depends mainly on the VC dimension and the dimensionality of the data. By reducing the VC dimension of the SVM classifier, its generalization performance is expected to increase. In the present paper, we argue that the VC dimension of SVM classifier can be reduced by applying bootstrapping and dimensionality reduction techniques. Experimental results showed that bootstrapping the original data and bootstrapping the projected (dimensionally reduced) data improved the performance of the SVM classifier.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

Multi-script and multi-oriented text localization from scene images

This paper describes a new method of color text localization from generic scene images containing text of different scripts and with arbitrary orientations. A representative set of colors is first identified using the edge information to initiate an unsupervised clustering algorithm. Text components are identified from each color layer using a combination of a support vector machine and a neural network classifier trained on a set of low-level features derived from the geometric, boundary, stroke and gradient information. Experiments on camera-captured images that contain variable fonts, size, color, irregular layout, non-uniform illumination and multiple scripts illustrate the robustness of the method. The proposed method yields precision and recall of 0.8 and 0.86 respectively on a database of 100 images. The method is also compared with others in the literature using the ICDAR 2003 robust reading competition dataset.

Combination of single walled carbon nanotubes/graphene oxide with paclitaxel: a reactive oxygen species mediated synergism for treatment of lung cancer

Heterogeneity in tumors has led to the development of combination therapies that enable enhanced cell death. Previously explored combination therapies mostly involved the use of bioactive molecules. In this work, we explored a non-conventional strategy of using carbon nanostructures (CNs) single walled carbon nanotube (SWNT) and graphene oxide (GO)] for potentiating the efficacy of a bioactive molecule paclitaxel (Tx)] for the treatment of lung cancer. The results demonstrated enhanced cell death following combination treatment of SWNT/GO and Tx indicating a synergistic effect. In addition, synergism was abrogated in the presence of an anti-oxidant, N-acetyl cysteine (NAC), and was therefore shown to be reactive oxygen species (ROS) dependent. It was further demonstrated using bromodeoxyuridine (BrdU) incorporation assay that treatment with CNs was associated with enhanced mitogen associated protein kinase (MAPK) activation that was ROS mediated. Hence, these results for the first time demonstrated the potential of SWNT/GO as co-therapeutic agents with Tx for the treatment of lung cancer.

A regularized linear classifier for effective text classification

In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.

Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing

For improved water management and efficiency of use in agriculture, studies dealing with coupled crop-surface water-groundwater models are needed. Such integrated models of crop and hydrology can provide accurate quantification of spatio-temporal variations of water balance parameters such as soil moisture store, evapotranspiration and recharge in a catchment. Performance of a coupled crop-hydrology model would depend on the availability of a calibrated crop model for various irrigated/rainfed crops and also on an accurate knowledge of soil hydraulic parameters in the catchment at relevant scale. Moreover, such a coupled model should be designed so as to enable the use/assimilation of recent satellite remote sensing products (optical and microwave) in order to model the processes at catchment scales. In this study we present a framework to couple a crop model with a groundwater model for applications to irrigated groundwater agricultural systems. We discuss the calibration of the STICS crop model and present a methodology to estimate the soil hydraulic parameters by inversion of crop model using both ground and satellite based data. Using this methodology we demonstrate the feasibility of estimation of potential recharge due to spatially varying soil/crop matrix.

Design and Evaluation of a Robust Optical Beam-Interruption-Based Vehicle Classifier System

This paper presents the design and development of a novel optical vehicle classifier system, which is based on interruption of laser beams, that is suitable for use in places with poor transportation infrastructure. The system can estimate the speed, axle count, wheelbase, tire diameter, and the lane of motion of a vehicle. The design of the system eliminates the need for careful optical alignment, whereas the proposed estimation strategies render the estimates insensitive to angular mounting errors and to unevenness of the road. Strategies to estimate vehicular parameters are described along with the optimization of the geometry of the system to minimize estimation errors due to quantization. The system is subsequently fabricated, and the proposed features of the system are experimentally demonstrated. The relative errors in the estimation of velocity and tire diameter are shown to be within 0.5% and to change by less than 17% for angular mounting errors up to 30 degrees. In the field, the classifier demonstrates accuracy better than 97.5% and 94%, respectively, in the estimation of the wheelbase and lane of motion and can classify vehicles with an average accuracy of over 89.5%.

Pre-clinical toxicity & immunobiological evaluation of DNA rabies vaccine & combination rabies vaccine in rhesus monkeys (Macaca mulatta)

Background & objectives: Pre-clinical toxicology evaluation of biotechnology products is a challenge to the toxicologist. The present investigation is an attempt to evaluate the safety profile of the first indigenously developed recombinant DNA anti-rabies vaccine DRV (100 mu g)] and combination rabies vaccine CRV (100 mu g DRV and 1.25 IU of cell culture-derived inactivated rabies virus vaccine)], which are intended for clinical use by intramuscular route in Rhesus monkeys. Methods: As per the regulatory requirements, the study was designed for acute (single dose - 14 days), sub-chronic (repeat dose - 28 days) and chronic (intended clinical dose - 120 days) toxicity tests using three dose levels, viz. therapeutic, average (2x therapeutic dose) and highest dose (10 x therapeutic dose) exposure in monkeys. The selection of the model i.e. monkey was based on affinity and rapid higher antibody response during the efficacy studies. An attempt was made to evaluate all parameters which included physical, physiological, clinical, haematological and histopathological profiles of all target organs, as well as Tiers I, II, III immunotoxicity parameters. Results: In acute toxicity there was no mortality in spite of exposing the monkeys to 10XDRV. In sub chronic and chronic toxicity studies there were no abnormalities in physical, physiological, neurological, clinical parameters, after administration of test compound in intended and 10 times of clinical dosage schedule of DRV and CRV under the experimental conditions. Clinical chemistry, haematology, organ weights and histopathology studies were essentially unremarkable except the presence of residual DNA in femtogram level at site of injection in animal which received 10X DRV in chronic toxicity study. No Observational Adverse Effects Level (NOAEL) of DRV is 1000 ug/dose (10 times of therapeutic dose) if administered on 0, 4, 7, 14, 28th day. Interpretation & conclusions: The information generated by this study not only draws attention to the need for national and international regulatory agencies in formulating guidelines for pre-clinical safety evaluation of biotech products but also facilitates the development of biopharmaceuticals as safe potential therapeutic agents.

Zoledronic acid in combination with alfacalcidol has additive effects on trabecular microarchitecture and mechanical properties in osteopenic ovariectomized rats

We conducted the present study to investigate the therapeutic effects of the antiresorptive agent zoledronic acid (ZOL), alone and in combination with alfacalcidol (ALF), in a rat model of postmenopausal osteoporosis. Female Wistar rats were ovariectomized (OVX) or sham-operated at 3 months of age. Twelve weeks post surgery, rats were randomized into six groups: (1) sham + vehicle, (2) OVX + vehicle, (3) OVX + ZOL (100 mu g/kg, i.v. single dose), (4) OVX + ZOL (50 mu g/kg, i.v. single dose), (5) OVX + ALF (0.5 mu g/kg, oral gauge daily) and (6) OVX + ZOL (50 mu g/kg, i.v. single dose) + ALF (0.5 mu g/kg, oral gauge daily) for 12 weeks. After treatment, we evaluated the mechanical properties of the lumbar vertebra and femoral mid-shaft. Femurs were also tested for bone density, porosity and trabecular micro-architecture. Biochemical markers in serum and urine were also determined. With respect to improvement in the mechanical strength of the lumbar spine and the femoral mid-shaft, the combination treatment of ZOL and ALF was more effective than each administered as a monotherapy. Moreover, combination therapy using ZOL and ALF preserved the trabecular micro-architecture and cortical bone porosity. Furthermore, the combination treatment of ZOL and ALF corrected the decrease in serum calcium and increase in serum alkaline phosphatase and the tartarate-resistant acid phosphatase level better than single-drug therapy using ZOL or ALF in OVX rats. In addition, the combination treatment of ZOL and ALF corrected the increase in urine calcium, phosphorous and creatinine levels better than single-drug therapy using ZOL or ALF in OVX rats. These data suggest that the combination treatment of ZOL and ALF has a therapeutic advantage over each monotherapy for the treatment of osteoporosis.

Novel Design of Honeycombs Using a Seamless Combination of Auxetic and Conventional Cores Toward Phononic Band Gap Engineering

A novel design for the geometric configuration of honeycombs using a seamless combination of auxetic and conventional cores- elements with negative and positive Possion ratios respectively, has been presented. The proposed design has been shown to generate a superior band gap property while retaining all major advantages of a purely conventional or purely auxetic honeycomb structure. Seamless combination ensures that joint cardinality is also retained. Several configurations involving different degree of auxeticity and different proportions auxetic and conventional elements have been analyzed. It has been shown that the preferred configurations open up wide and clean band gap at a significantly lower frequency ranges compared to their pure counterparts. In view of existence of band gaps being desired feature for the phononic applications, reported results might be appealing. Use of such design may enable superior vibration control as well. Proposed configurations can be made isovolumic and iso-weight giving designers a fairer ground of applying such configurations without significantly changing size and weight criteria.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Cultivation of Pleurotus spp. on a combination of anaerobically digested plant material and various agro-residues

Two species of Pleurotus, Pleurotus florida and Pleurotus flabellatus were cultivated on two agro-residues (paddy straw; PS and coir pith; CP) singly as well as in combination with biogas digester residue (BDR, main feed leaf biomass). The biological efficiency, nutritional value, composition and nutrient balance (C, N and P) achieved with these substrates were studied. The most suitable substrate that produced higher yields and biological efficiency was PS mixed with BDR followed by coir pith with BDR. Addition of BDR with agro-residues could increase mushroom yield by 20-30%. The biological efficiency achieved was high for PS + BDR (231.93% for P. florida and 209.92% for P. flabellatus) and for CP + BDR (14831% for P. florida and 188.46% for P. flabellatus). The OC (organic carbon), TKN (nitrogen) and TP (phosphate) removal of the Pleurotus spp. under investigation suggests that PS with BDR is the best substrate for growing mushroom. (C) 2015 Published by Elsevier Inc. on behalf of International Energy Initiative.

Combination of Cyanine Behaviour and Giant Hyperpolarisability in Novel Merocyanine Dyes: Beyond the Bond Length Alternation (BLA) Paradigm

Merocyanine dyes that exhibit antithetic cyaninelike behaviour and giant first-order hyperpolarisability (beta) values have been designed. These cyanine-type dyes open up an intriguing route towards molecular-based electrooptic materials as well as new second-harmonic generation dyes for imaging.

Chronic lung infection by Pseudomonas aeruginosa biofilm is cured by L-Methionine in combination with antibiotic therapy

Bacterial biofilms are associated with 80-90% of infections. Within the biofilm, bacteria are refractile to antibiotics, requiring concentrations >1,000 times the minimum inhibitory concentration. Proteins, carbohydrates and DNA are the major components of biofilm matrix. Pseudomonas aeruginosa (PA) biofilms, which are majorly associated with chronic lung infection, contain extracellular DNA (eDNA) as a major component. Herein, we report for the first time that L-Methionine (L-Met) at 0.5 mu M inhibits Pseudomonas aeruginosa (PA) biofilm formation and disassembles established PA biofilm by inducing DNase expression. Four DNase genes (sbcB, endA, eddB and recJ) were highly up-regulated upon L-Met treatment along with increased DNase activity in the culture supernatant. Since eDNA plays a major role in establishing and maintaining the PA biofilm, DNase activity is effective in disrupting the biofilm. Upon treatment with L-Met, the otherwise recalcitrant PA biofilm now shows susceptibility to ciprofloxacin. This was reflected in vivo, in the murine chronic PA lung infection model. Mice treated with L-Met responded better to antibiotic treatment, leading to enhanced survival as compared to mice treated with ciprofloxacin alone. These results clearly demonstrate that L-Met can be used along with antibiotic as an effective therapeutic against chronic PA biofilm infection.

A Bacterial Foraging Optimization and Learning Automata Based Feature Selection for Motor Imagery EEG Classification

Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In this study, we have used a combination of Bacterial Foraging Optimization and Learning Automata to determine the best subset of features from a given motor imagery electroencephalography (EEG) based BCI dataset. Here, we have employed Discrete Wavelet Transform to obtain a high dimensional feature set and classified it by Distance Likelihood Ratio Test. Our proposed feature selector produced an accuracy of 80.291% in 216 seconds.