56 research outputs found
Extraction of bioactive conserves from curry leaves
No full textThis Dissertation / Report is the outcome of investigation carried out by the creator(s) / author(s) at the department/division of Central Food Technological Research Institute (CFTRI), Mysore mentioned below in this page
Cross Domain Recommendation Using Semantic Similarity and Tensor Decomposition
No full textAbstractOnline shopping has become the buzzword in this information age. Users want to purchase the best possible item and services at the shortest span of time. In this information age Recommender system is a very useful tool, because it has the capability of filtering the information according to user interest and provide personalized suggestion. One of the major drawbacks of the classical recommender system is that, they deal with the only single domain. In real world scenario domains could be related to each other by some common information. There are many approaches available for cross domain recommendation, but they are not able to provide better accuracy of high dimensional data and these approaches are suffering from data sparsity problem. In this paper, we deal with cross domain recommendation where we exploit knowledge from auxiliary domains (e.g., movies) which contains additional user preference data to improve recommendation on the target domain (e.g., books). In order to achieve a high level of accuracy, we make use of semantic similarity measure of common information by which domains are related and Tensor decomposition to exploiting the latent factor for high dimensional data. Tensor decomposition with semantic similarity is used for making cross domain recommendation where in the data sparsity problem is avoided by normalizing and clustering the data in auxiliary domain. We provide experimental results on real world data sets and compared our proposed method with other similar approaches based on hit ratio and the results show that we achieve a better hit ratio
Integrated Multiple Features for Tumor Image Retrieval Using Classifier and Feedback Methods
No full textAbstractThe content based image retrieval method greatly assists in retrieving medical images close to the query image from a large database basing on their visual features. This paper presents an effective approach in which the region of the object is extracted with the help of multiple features ignoring the background of the object by employing edge following segmentation method followed by extracting texture and shape characteristics of the images. The former is extracted with the help of Steerable filter at different orientations and radial Chebyshev moments are used for extracting the later. Initially the images similar to the query image are extracted from a large group of medical images. Then the search is by accelerating the retrieval process with the help of Support Vector Machine (SVM) classifier. The performance of the retrieval system is enhanced by adapting the subjective feedback method. The experimental results show that the proposed region based multiple features and integrated with classifier and subjective feedback method yields better results than classical retrieval systems
M-FFN: multi-scale feature fusion network for image captioning
No full textIn this work, we present a novel multi-scale feature fusion network (M-FFN) for image captioning task to incorporate discriminative features and scene contextual information of an image. We construct multi-scale feature fusion network by leveraging spatial transformation and multi-scale feature pyramid networks via feature fusion block to enrich spatial and global semantic information. In particular, we take advantage of multi-scale feature pyramid network to incorporate global contextual information by employing atrous convolutions on top layers of convolutional neural network (CNN). And, the spatial transformation network is exploited on early layers of CNN to remove intra-class variability caused by spatial transformations. Further, the feature fusion block integrates both global contextual information and spatial features to encode the visual information of an input image. Moreover, spatial-semantic attention module is incorporated to learn attentive contextual features to guide the captioning module. The efficacy of the proposed model is evaluated on the COCO dataset. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
Optimization of combustion characteristics on a diesel engine fueled by Mahua biodiesel with dispersion of graphene oxide and zinc oxide nanoparticles as additives using design of experiment
No full textThe current research investigates the effects of adding metallic graphene oxide (GO) and non-metallic zinc oxide (ZnO) nanoparticles to Mahua biodiesel blend (B20) on the combustion parameters of a diesel engine. GO and ZnO nanoparticles were utilized at a concentration of 75 mg/L, combined with a 1:1 mixture of the surfactant CTAB and the dispersant TWEEN 80. When nanoparticles were introduced to blended biofuel, combustion parameters such as cumulative heart rate, mean gas temperature, mass percent burnt, and rise of pressure increase (RoPR) greatly improved at higher injection pressures. When compared to clean diesel, utilizing B20+ZnO Nanoparticles+ NIS dispersant at 250 bar resulted in 6%, 15%, 7%, and 7.6% improvements in CHRR, MGT, MFB, and RoPR, respectively. The correlation coefficient (R2) for B20+ZnO NPs+ NIS (1:1) for CHRR, MGT, MFB and RoPR is 0.975, 0.978, 0.966 and 0.9883 when compared to GO nanoparticle inclusions, considering it as optimum combination and an efficient fuel. When compared to other fuel samples, the CHRR, MGT, MFB and RoPR for B20+ZnO NPs+ NIS are 2.484%, 3.2%, 2.6% and 1.25% higher, respectively, according to a statistical analysis conducted by design expert
Optimization of combustion characteristics on a diesel engine fueled by Mahua biodiesel with dispersion of graphene oxide and zinc oxide nanoparticles as additives using design of experiment
No full textThe current research investigates the effects of adding metallic graphene oxide (GO) and non-metallic zinc oxide (ZnO) nanoparticles to Mahua biodiesel blend (B20) on the combustion parameters of a diesel engine. GO and ZnO nanoparticles were utilized at a concentration of 75 mg/L, combined with a 1:1 mixture of the surfactant CTAB and the dispersant TWEEN 80. When nanoparticles were introduced to blended biofuel, combustion parameters such as cumulative heart rate, mean gas temperature, mass percent burnt, and rise of pressure increase (RoPR) greatly improved at higher injection pressures. When compared to clean diesel, utilizing B20+ZnO Nanoparticles+ NIS dispersant at 250 bar resulted in 6%, 15%, 7%, and 7.6% improvements in CHRR, MGT, MFB, and RoPR, respectively. The correlation coefficient (R2) for B20+ZnO NPs+ NIS (1:1) for CHRR, MGT, MFB and RoPR is 0.975, 0.978, 0.966 and 0.9883 when compared to GO nanoparticle inclusions, considering it as optimum combination and an efficient fuel. When compared to other fuel samples, the CHRR, MGT, MFB and RoPR for B20+ZnO NPs+ NIS are 2.484%, 3.2%, 2.6% and 1.25% higher, respectively, according to a statistical analysis conducted by design expert
Finite element analysis for mechanical response of magnesium foams with regular structure obtained by powder metallurgy method
No full textAbstractMagnesium and Magnesium alloys have attracted immense attention as a biomedical implant material due to favourable mechanical properties and biocompatibility. Biodegradable nature of Magnesium dismisses the need of revision surgery for removal of implant. Porous Mg- foams are advantageous as presence of pores allows the higher degree of osseointegration. The mechanical properties of the porous foam material is a function of its density, thus a Finite Element Method (FEM) approach is required to predict the behaviour of Mg- foam under various stresses for real-time application. The author has attempted to quantitatively assess the mechanical properties of Mg foam with a 40-45% porosity with 100-300μm pore size. The deformation behaviour of Mg- foams with different porosity under the compressive and bending loads has been described by “Deshpande and Fleck model” with ABAQUS FEM software. The simulation results have been compared with the recent publications. An agreeable comparison has been seen in the results
An investigative and evaluative study of factors affecting quality of agricultural and farm information services in Kerala
No full textAgriculture is not only a country’s backbone of food, livelihood and ecological security systems, but is also the very soul of its sovereignty. In Kerala population density is high and land is scarce. To achieve sustainable advancement in quality of human life, meeting the domestic food requirement is to be given foremost priority in development plans. As the area of cultivation cannot be increased and growth of population cannot be controlled growth in food production is to be achieved by qualitative improvement in farming. This requires improvements in material inputs, farming techniques, storage technology and research. Effective integration of these factors is tied closely to adequate information flow, which can be ensured only by an efficient information system for agricultural education, research, extension and development. So evaluation and improvement of existing information services is very crucial for sustainable agricultural growth. The study evaluates the existing information resources, facilities, services, possibilities for resource sharing, accessibility of external sources, and the factors that affect the quality and efficiency of information services in agricultural sector. Coverage is limited to the State of Kerala. Sample consist 105 institutions of different levels, and information users consisting of 426 scientists and 220 farmers. Different sets of questionnaires and interview schedule were used to elicit information. The study found that agricultural research conducted at various institutions in the region at huge public expense has generated knowledge for improving production. Along with these huge collections of acquired content is also stored in the sector. But when a farmer, an extension worker, a scientist or an administrator needs information it is not easily accessible. The study found that agricultural sector fails to effectively bank on information resources available due to the lack of an information system and network. Recommends an Agricultural and Farm Information System for Kerala. Suggests a model plan for a computer communication network for resource sharing between the agricultural institutions in the State, which will also ensure, smooth flow of results of research down to the grassroots level to achieve maximum productivity in agriculture
U-Pb dating of metamorphic monazite establishes a Pan-African age for tectonism in the Nallamalai Fold Belt, India
No full text© 2017 The Author(s). The Nallamalai Fold Belt comprises late Palaeoproterozoic to Mesoproterozoic sedimentary rocks deformed into a fold-and-thrust belt along the eastern side of Peninsular India. The age of thin-skinned thrusting, folding and low- to mediumgrade metamorphism in the belt is unclear, with estimates ranging from Palaeoproterozoic to early Palaeozoic. A possible Pan- African age for thrusting has previously been inferred from Rb-Sr dating of muscovite in shear zones from the adjacent Krishna Province (501 - 474 Ma) but these structures are separated from the Nallamalai Fold Belt by a major thrust. Here, we present in situ U-Pb dating of metamorphic monazite within a low-grade metasedimentary rock in the Nallamalai Fold Belt at the Mangampeta barite mine. Our date of 531 ± 7 Ma for the monazite is the first direct evidence that west- to NW-directed nappe stacking, folding and low-grade metamorphism in the fold belt are related to Pan-African incorporation of India into the Gondwana supercontinent
A convergent approach towards the synthesis of the 2-alkyl-substituted tetrahydroquinoline alkaloid (−)-cuspareine
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