Designing a Framework of Ethnomedicinal Plant Knowledge Integration Using OSS

Indigenous knowledge refers to the knowledge, innovations, and practices of indigenous communities. Ethnic groups are repository knowledge of herbal medicine. Many indigenous people use several plants for medicinal preparations, and these medicines are known as ethnomedicine. It has developed from experience gained over centuries. Species of ethnomedicinal plants are threatened in most of nations due to overexploitation, habitat loss, destructive harvesting techniques, unsustainable trade, and deforestation. Documented indigenous knowledge on ethnomedicine forms part of the documentary heritage of the nation. The chapter will provide a framework for design an information retrieval system for ethnomedicine or knowledge on medicinal plants that are used to manage human ailments. The framework will be prepared, established on the open source software (OSS), and is appropriate not only for documentation but also beneficial for retrieving domain-specific knowledge. The model provides a framework for resource integration digitally using Greenstone Digital Library (GSDL) software.

Genealogical and zootechnical characteristics of the breeding core of Akhal-Teke horses of the Stavropol Territory

The purpose of the study is to analyze the pedigree core of the Akhal-Teke breed bred in the Stavropol Territory using the example of the leading breeding farm LLC “Stavropol stud farm No. 170”. The object of the study was stud stallions (n = 5) and brood mares (n = 30) of the thoroughbred Akhal-Teke breed. Information sources of research: statements of results of assessment of pedigree horses, catalogs of stallions-producers, state studbooks of Akhal-Teke horses, data from the information retrieval system HORSES-3. Results and scope of application. The stallions-producers of the Akhal-Teke breed used in the LLC “Stavropol stud farm No. 170” belong to 4 lines: El, Posman, Gelishikli and Fakirpelvan. According to the direct male line, all mares of the breeding core belong to 6 lines: Gelishikli (36.7 %), El (23.3 %), Gaplan (16.7 %), Posman (13.3 %), Fakirpelvan (6.7 %) and Sere (3.3 %). Zootechnical assessment of breeding stallions and mares of the breeding core showed their compliance with the breed standard. The average age of breeding stallions is 18 years, and broodmares – 11.7 years. The research results can be recommended as an educational material for students and undergraduates of universities studying in the areas of zootechnical profile training, and can also be used in the practical work of zootechnicians of breeding farms and private individuals engaged in breeding Akhal-Teke horses. The scientific novelty of the research lies in the fact that for the first time a detailed genealogical and zootechnical assessment of the breeding nucleus of the Akhal-Teke horses bred in the Stavropol Territory has been given.

Multidimensional indexing technique for medical images retrieval

Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines.

Quem@PUC - A tool to find researchers at PUC-Rio

Quem@PUC is an Information Retrieval System available on the Web that allows searching for researchers and professors based on a keyword list of research related terms. It publicizes research and teaching activities from the PUC-Rio community to society in general. The idea is to integrate information from professors from administrative systems, courses offered, and researchers’ Lattes CVs. Data sources are converted to RDF format using domain ontologies, then stored in a NoSQL database that supports native free-text indexing on triple objects. Search results include names, academic papers, teaching activities, and contact links.

Criminal Records Information Retrieval System: A Conceptual Model

This model helps to increase communication between Police and public. It will reduce time & increase the problem solving efficiency in time period it will be more helpful. In this admin is key person, user(police) is also have secure registration & public can communicate with all other users through mail. Here we are going to use clustering technique because it more powerful to forming accurate cluster, speed of creating cluster, identifying crime trend & crime zone ,crime density of state.

A two-stage information retrieval system based on interactive multimodal genetic algorithm for query weight optimization

Query weight optimization, which aims to find an optimal combination of the weights of query terms for sorting relevant documents, is an important topic in the information retrieval system. Due to the huge search space, the query optimization problem is intractable, and evolutionary algorithms have become one popular approach. But as the size of the database grows, traditional retrieval approaches may return a lot of results, which leads to low efficiency and poor practicality. To solve this problem, this paper proposes a two-stage information retrieval system based on an interactive multimodal genetic algorithm (IMGA) for a query weight optimization system. The proposed IMGA has two stages: quantity control and quality optimization. In the quantity control stage, a multimodal genetic algorithm with the aid of the niching method selects multiple promising combinations of query terms simultaneously by which the numbers of retrieved documents are controlled in an appropriate range. In the quality optimization stage, an interactive genetic algorithm is designed to find the optimal query weights so that the most user-friendly document retrieval sequence can be yielded. Users’ feedback information will accelerate the optimization process, and a genetic algorithm (GA) performs interactively with the action of relevance feedback mechanism. Replacing user evaluation, a mathematical model is built to evaluate the fitness values of individuals. In the proposed two-stage method, not only the number of returned results can be controlled, but also the quality and accuracy of retrieval can be improved. The proposed method is run on the database which with more than 2000 documents. The experimental results show that our proposed method outperforms several state-of-the-art query weight optimization approaches in terms of the precision rate and the recall rate.