Ir_urfs_vf: Image Recommendation with User Relevance Feedback Session and Visual Features in Vertical Image Search

In recent years, online shopping has grown exponentially and huge number of images are available online. Hence, it is necessary to recommend various product images to aid the user in effortless and efficient access to the desired products. In this paper, we present image recommendation framework with user relevance feedback session and visual features (IR_URFS_VF) to extract relevant images based on user inputs. User feedback is retrieved from image search history with clicked and un-clicked images. Image features are computed off-line and later used to find relevance between images. The relevance between images is determined by cosine similarity and are ranked based on clicked frequency and similarity score between images. Experiments results show that IR_URFS_VF outperforms CBIR method by providing more relevant ranked images to the user input query

Performance Based Plastic Design of Concentrically Braced Frame attuned with Indian Standard code and its Seismic Performance Evaluation

In the Performance Based Plastic design method, the failure is predetermined; making it famous throughout the world. But due to lack of proper guidelines and simple stepwise methodology, it is not quite popular in India. In this paper, stepwise design procedure of Performance Based Plastic Design of Concentrically Braced frame attuned with the Indian Standard code has been presented. The comparative seismic performance evaluation of a six storey concentrically braced frame designed using the displacement based Performance Based Plastic Design (PBPD) method and currently used force based Limit State Design (LSD) method has also been carried out by nonlinear static pushover analysis and time history analysis under three different ground motions. Results show that Performance Based Plastic Design method is superior to the current design in terms of displacement and acceleration response. Also total collapse of the frame is prevented in the PBPD frame

Image Recommendation Based on Keyword Relevance Using Absorbing Markov Chain and Image Features

Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images

ACSIR: ANOVA Cosine Similarity Image Recommendation in vertical search

In today�s world, online shopping is very attractive and grown exponentially due to revolution in digitization. It is a crucial demand to provide recommendation for all the search engine to identify users� need. In this paper, we have proposed a ANOVA Cosine Similarity Image Recommendation (ACSIR) framework for vertical image search where text and visual features are integrated to fill the semantic gap. Visual synonyms of each term are computed using ANOVA p value by considering image visual features on text-based search. Expanded queries are generated for user input query, and text-based search is performed to get the initial result set. Pair-wise image cosine similarity is computed for recommendation of images. Experiments are conducted on product images crawled from domain-specific site. Experiment results show that the ACSIR outperforms iLike method by providing more relevant products to the user input query. © 2017, Springer-Verlag London

A comparison of intrathecal dexmedetomidine and clonidine as adjuvants to hyperbaric bupivacaine for gynecological surgery

Background: Various adjuvants are being used with local anesthetics for prolongation of intraoperative and post-operative analgesia. Dexmedetomidine, a highly selective alpha2 adrenergic agonist, is a new neuraxial adjuvant gaining popularity. The purpose of this study was to compare the onset, duration of sensory and motor block, hemodynamic effects, post-operative analgesia, and adverse effects of dexmedetomidine and clonidine with hyperbaric 0.5% bupivacaine for spinal anesthesia.Methods: 60 patients belonging to ASA Grade 1 and 2 undergoing elective gynecological surgery under spinal anesthesia were studied in this prospective. The patients were allocated in two groups (30 patients each). Group bupivacaine + clonidine (BC) received 17.5 mg of bupivacaine supplemented 45 mcg clonidine and Group bupivacaine + dexmedetomidine (BD) received 17.5 mg bupivacaine supplemented 5 mcg dexmedetomidine. The onset time of sensory and motor level, time to reach peak sensory and motor level, the regression time of sensory and motor level, hemodynamic changes, and side effects were recorded.Results: Patients in Group BD had significantly longer sensory and motor block time than patients in Group BC. The onset time to reach dermatome T4 and modified Bromage3 motor block were not significantly different between two groups. Dexmedetomidine group showed significantly less and delayed requirement of rescue analgesic.Conclusion: Intrathecal dexmedetomidine is associated with prolonged motor and sensory block, hemodynamic stability and reduced demand of rescue analgesic in 24 hrs as compared to clonidine

IRAbMC: Image Recommendation with Absorbing Markov Chain

Image Recommendation is an important feature for search engine as tremendous amount images are available online. It is necessary to retrieve relevant images to meet user's requirement. In this paper, we present an algorithm Image Recommendation with Absorbing Markov Chain (IRAbMC) to retrieve relevant images for user input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Absorbing Markov chain is used to calculate keyword relevance. Experiments results show that the IRAbMC algorithm outperforms Markovian Semantic Indexing (MSI) method with improved relevance score of retrieved ranked images

Query Click and Text Similarity Graph for Query Suggestions

Query suggestion is an important feature of the search engine with the explosive and diverse growth of web contents. Different kind of suggestions like query, image, movies, music and book etc. are used every day. Various types of data sources are used for the suggestions. If we model the data into various kinds of graphs then we can build a general method for any suggestions. In this paper, we have proposed a general method for query suggestion by combining two graphs: (1) query click graph which captures the relationship between queries frequently clicked on common URLs and (2) query text similarity graph which finds the similarity between two queries using Jaccard similarity. The proposed method provides literally as well as semantically relevant queries for users’ need. Simulation results show that the proposed algorithm outperforms heat diffusion method by providing more number of relevant queries. It can be used for recommendation tasks like query, image, and product suggestion

How to assess pharmacogenomic tests for implementation in the NHS in England

AIMS: Pharmacogenomic testing has the potential to target medicines more effectively towards those who will benefit and avoid use in individuals at risk of harm. Health economies are actively considering how pharmacogenomic tests can be integrated into health care systems to improve use of medicines. However, one of the barriers to effective implementation is evaluation of the evidence including clinical usefulness, cost-effectiveness, and operational requirements. We sought to develop a framework that could aid the implementation of pharmacogenomic testing. We take the view from the National Health Service (NHS) in England. METHODS: We used a literature review using EMBASE and Medline databases to identify prospective studies of pharmacogenomic testing, focusing on clinical outcomes and implementation of pharmacogenomics. Using this search, we identified key themes relating to the implementation of pharmacogenomic tests. We used a clinical advisory group with expertise in pharmacology, pharmacogenomics, formulary evaluation, and policy implementation to review data from our literature review and the interpretation of these data. With the clinical advisory group, we prioritized themes and developed a framework to evaluate proposals to implement pharmacogenomics tests. RESULTS: Themes that emerged from review of the literature and subsequent discussion were distilled into a 10-point checklist that is proposed as a tool to aid evidence-based implementation of pharmacogenomic testing into routine clinical care within the NHS. CONCLUSION: Our 10-point checklist outlines a standardized approach that could be used to evaluate proposals to implement pharmacogenomic tests. We propose a national approach, taking the view of the NHS in England. Using this approach could centralize commissioning of appropriate pharmacogenomic tests, reduce inequity and duplication using regional approaches, and provide a robust and evidence-based framework for adoption. Such an approach could also be applied to other health systems

Conversion Prediction for Advertisement Recommendation using Expectation Maximization

Advertiser has to understand the purchase require-ment of the users who are looking for a particular service to recommend advertisement. Once the users’ demand is identified, advertisers can target those users with appropriate query. In this paper, predicting conversion in advertising using expectation maximization [PCAEM] model is proposed to provide influence of their advertising campaigns to the advertisers by understanding hidden topics in search terms with respect to the time period. Query terms present in search log are used to construct vocabulary. Expectation Maximization technique is used to learn hidden topics from the vocabulary. Least Absolute Shrinkage and Selection Operator (LASSO) is used to predict total number of conversion. Experiment results show that PCAEM model outperforms TopicMachine model by reducing Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) for prediction

The School Counselor STEM Advocacy Survey (SC-STEM-AS)

We examined data from a national sample of 917 school counselors to determine the factor structure of the School Counselor STEM Advocacy Survey. An exploratory and confirmatory factor analysis supported use of the two-factor model. Survey scores demonstrated good internal consistency and convergent validity. We discuss differences between key demographics and school counselors