Information Retrieval Methods Research Articles

Research on the Application of MOSL Information Retrieval Method in Educational Resource Management

Research on the Application of MOSL Information Retrieval Method in Educational Resource Management

Ethics information retrieval in HTA: state of current practice.

Though there have been longstanding discussions on the value of ethics in health technology assessment (HTA), less awareness exists on ethics information retrieval methods. This study aimed to scope available evidence and determine current practices for ethics information retrieval in HTA. Literature searches were conducted in Ovid MEDLINE, LISTA, Scopus, and Google Scholar. Once a list of relevant articles was determined, citation tracking was conducted via Scopus. HTA agency websites were searched for published guidance on ethics searching, and for reports which included ethical analyses. Methods sections of each report were analyzed to determine the databases, subject headings, and keywords used in search strategies. The team also reached out to information specialists for insight into current search practices. Findings from this study indicate that there is still little published guidance from HTA agencies, few HTAs that contain substantial ethical analysis, and even less information on the methodology for ethics information retrieval. The researchers identified twenty-five relevant HTAs. Ten of these reports did not utilize subject-specific databases outside health sciences. Eight reports published ethics searches, with significant overlap in subject headings and text words. This scoping study of current practice in HTA ethics information retrieval highlights findings of previous studies-while ethics analysis plays a crucial role in HTA, methods for literature searching remain relatively unclear. These findings provide insight into the current state of ethics searching, and will inform continued work on filter development, database selection, and grey literature searching.

Benchmarking for Recommender System (MFRISE)

The advent of the internet age offers overwhelming choices of movies and shows to viewers which create need of comprehensive Recommendation Systems (RS). Recommendation System will suggest best content to viewers based on their choice using the methods of Information Retrieval, Data Mining and Machine Learning algorithms. The novel Multifaceted Recommendation System Engine (MFRISE) algorithm proposed in this paper will help the users to get personalized movie recommendations based on multi-clustering approach using user cluster and Movie cluster along with their interaction effect. This will add value to our existing parameters like user ratings and reviews. In real-world scenarios, recommenders have many non-functional requirements of technical nature. Evaluation of Multifaceted Recommendation System Engine must take these issues into account in order to produce good recommendations. The paper will show various technical evaluation parameters like RMSE, MAE and timings, which can be used to measure accuracy and speed of Recommender system. The benchmarking results also helpful for new recommendation algorithms. The paper has used MovieLens dataset for purpose of experimentation. The studied evaluation methods consider both quantitative and qualitative aspects of algorithm with many evaluation parameters like mean squared error (MSE), root mean squared error (RMSE), Test Time and Fit Time are calculated for each popular recommender algorithm (NMF, SVD, SVD++, SlopeOne, Co- Clustering) implementation. The study identifies the gaps and challenges faced by each above recommender algorithm. This study will also help researchers to propose new recommendation algorithms by overcoming identified research gaps and challenges of existing algorithms.

Machine learning and ontology-based novel semantic document indexing for information retrieval

The goal of information retrieval (IR) systems is to find the contents most closely related to the user's information needs from a pool of information. However, conventional IR methods neglect semantic descriptions of document contents and index documents based on the words that they include. When users and indexing systems use different terms to express the same subject, a vocabulary gap emerges. To overcome this limitation and to enhance the effectiveness of the IR systems, this paper introduced a novel hybrid semantic document indexing employing machine learning and domain ontology. The presented technique uses a skip-gram with negative sampling-based machine learning model and a domain ontology to determine the concepts for annotating unstructured documents. The proposed work also introduced multiple feature based novel concept ranking algorithm where statistical, semantic, and scientific named entity features of the concept were used to assign relevance weight to the annotations. The fuzzy analytical hierarchy process was used to derive the parameters of these feature weights. The final step is to rank the concepts according to their relevance to the document. Five benchmark publicly accessible datasets from the computer science domain were used in a series of experiments to validate the results of presented method. Experiment findings showed that the proposed method performs better than state-of-the-art techniques on these datasets, by improving average accuracy by 29%, while an improvement of 25% was recorded in F-measure. The improvement in average accuracy demonstrates that the performance of the proposed approach is better than the state-of-the-art methods in extracting document concepts accurately even when the same concept is referred to by distinct terms in the document and domain ontologies. The proposed system's ability to find similar concepts when the documents possess no concept from domain ontology is demonstrated by the improvement in F-measure, which is attributed to high recall rates of the proposed indexing scheme while maintaining high accuracy.

A Technology Watch/Competitive Intelligence–based Decision-Support System optimised with Genetic Algorithms

To survive and prosper in a highly competitive environment where uncertainty and ambiguity are the norm, today’s firms are faced with the need for new information management methods and tools. Two of the most prominent strategies that take information and its treatment as a value-generating element in firms’ decision-making are Technology Watch and Competitive Intelligence. In addition, one of the fundamental components that a system based on these strategies must have is an efficient method of Information Retrieval. The present study describes a Competitive Intelligence–based decision-support system that uses a Genetic Algorithm. The system contributes to improving information retrieval through search optimisation, thus enhancing the performance of this knowledge-generating tool for organisations.

Screening for Social Determinants of Health: Active and Passive Information Retrieval Methods.

Screening for social determinants of health (SDOH) is recommended, but numerous barriers exist to implementing SDOH screening in clinical spaces. In this study, the authors identified how both active and passive information retrieval methods may be used in clinical spaces to screen for SDOH and meet patient needs. The authors conducted a retrospective sequential cohort analysis comparing the active identification of SDOH through a patient-led digital manual screening process completed in primary care offices from September 2019 to January 2020 and passive identification of SDOH through natural language processing (NLP) from September 2016 to August 2018, among 1735 patients at a large midwestern tertiary referral hospital system and its associated outlying primary care and outpatient facilities. The percent of patients identified by both the passive and active identification methods as experiencing SDOH varied from 0.3% to 4.7%. The active identification method identified social integration, domestic safety, financial resources, food insecurity, transportation, housing, and stress in proportions ranging from 5% to 36%. The passive method contributed to the identification of financial resource issues and stress, identifying 9.6% and 3% of patients to be experiencing these issues, respectively. SDOH documentation varied by provider type. The combination of passive and active SDOH screening methods can provide a more comprehensive picture by leveraging historic patient interactions, while also eliciting current patient needs. Using passive, NLP-based methods to screen for SDOH will also help providers overcome barriers that have historically prevented screening.

AR Search Engine: Semantic Information Retrieval for Augmented Reality Domain

With the emergence of the metaverse, the popularity of augmented reality (AR) is increasing; accessing concise, accurate, and precise information in this field is becoming challenging on the world wide web. In regard to accessing the right information through search engines, semantic information retrieval via a semantic analysis delivers more relevant information pertaining to the user’s query. However, there is insufficient research on developing semantic information retrieval methods in the AR domain that ranks and clusters AR-based search results in a fair fashion. This paper develops an AR search engine that automatically organizes, understands, searches, and summarizes web documents to enhance the relevancy scores in AR domains. The engine enables users to organize and manage relevant AR documents in various AR concepts and efficiently discover more accurate results in terms of relevancy in the AR field. First, we propose an AR ontology for clustering AR documents into AR topics and concepts. Second, we developed an ontology-based clustering method using the k-means clustering algorithm, vector space model, and term frequency-inverse document frequency (TF-IDF) weighting model with ontology to explore and cluster the AR documents. Third, an experiment was designed to evaluate the proposed AR search engine and compare it with the custom search engine in the AR domains. The results showed that the AR search engine accessed the right information about 42.33% faster and with a 34% better ranking.

The principle and state-of-art applications of Hawking radiation

Hawking radiation, firstly discovered in 1974 by Stephen Hawking, is a crucial quantum phenomenon, which proves that a black hole has been losing its mass since its formation. In this paper, the method of information retrieval and literature analysis are fully applied to introduce the principle and state-of-art applications of Hawking radiation. Three parameters used to describe the evaporation of black holes are analyzed. Furthermore, the tunnelling effect of entangled pairs near event horizon is described to explain Hawking radiation from a microscopic perspective. Then, the evaporating black holes are observed under wave optical conditions by the Fourier transformation of the spatial correlation function and detected in Laboratory condition by equivalenting entangled pairs to surface wave packets with sum-zero energy. Finally, a prediction is made that the widely used of extra-dimensional theory and high-energy particle technology as well as numerical researches in laboratory conditions will be strongly pushing the observation of Hawking radiation. Overall, these results shed light on further exploring the universe in terms of Hawking radiation.

An Enhanced Information Retrieval Method Based on Ontology for Bridge Inspection

Bridge management systems (BMSs) are widely used to assist an inspector in performing element-level bridge inspection. Retrieving and determining target elements to be inspected becomes an important factor in the efficiency of bridge inspection. This paper presents an enhanced information retrieval (IR) method based on ontology to predict the target elements. The novelty of this method is that an improved seven-step method based on automatic mapping technology is proposed to construct a new bridge inspection ontology (BIontology), which provides a knowledge base for the present IR method. A further novelty is that a new software architecture is designed for integrating ontology, and a promising prototype system based on the software architecture is developed to realize the present IR method using SPARQL query. In addition, a novel prediction algorithm based on the present IR method is proposed to automatically recommend the target elements. A case study of ontology construction is performed to demonstrate that the improved seven-step method can accelerate the construction of the BIontology compared with the manual method. A case study of bridge inspection is implemented to verify that the proposed algorithm outperforms an existing method, thereby validating the effectiveness of the present IR method.

Drug Adverse Event Detection Using Text-Based Convolutional Neural Networks (TextCNN) Technique

With the rapid advancement in healthcare, there has been exponential growth in the healthcare records stored in large databases to help researchers, clinicians, and medical practitioner’s for optimal patient care, research, and trials. Since these studies and records are lengthy and time consuming for clinicians and medical practitioners, there is a demand for new, fast, and intelligent medical information retrieval methods. The present study is a part of the project which aims to design an intelligent medical information retrieval and summarization system. The whole system comprises three main modules, namely adverse drug event classification (ADEC), medical named entity recognition (MNER), and multi-model text summarization (MMTS). In the current study, we are presenting the design of the ADEC module for classification tasks, where basic machine learning (ML) and deep learning (DL) techniques, such as logistic regression (LR), decision tree (DT), and text-based convolutional neural network (TextCNN) are employed. In order to perform the extraction of features from the text data, TF-IDF and Word2Vec models are employed. To achieve the best performance of the overall system for efficient information retrieval and summarization, an ensemble strategy is employed, where predictions of the selected base models are integrated to boost the robustness of one model. The performance results of all the models are recorded as promising. TextCNN, with an accuracy of 89%, performs better than the conventional machine learning approaches, i.e., LR and DT with accuracies of 85% and 77%, respectively. Furthermore, the proposed TextCNN outperforms the existing adverse drug event classification approaches, achieving precision, recall, and an F1 score of 87%, 91%, and 89%, respectively.

Analysis and systematization of the main market trends development in pressurized pharmaceutical preparations in pharmaceutical and veterinary practice

Aim. Analysis and systematization of the main market trends development in pressurized pharmaceutical preparations in pharmaceutical and veterinary practice. Materials and methods. Methods of systematic approach, bibliographic methods, information retrieval, analysis, comparison and generalization, statistical processing, tabular and graphic means of visual presentation of the obtained data were used during the market research. The analysis of registered in Ukraine pressurized pharmaceutical preparations was carried out based on the data of the State Register of Medicinal Products of Ukraine, “Morion” information search program, the Anatomical Therapeutic Chemical (ATC) Classification of electronic resource Compendium.online and the list of registered veterinary medicinal products. Research results. The paper presents the results of the marketing analysis of the Ukraine pharmaceutical market of pressurized pharmaceutical preparations for medical and veterinary practice. As of April 2022, the total number of registered medicinal products is 65 names, of which 58 (89,23%) are for medical purposes and 7 (10,77%) for veterinary purposes. The studied drugs in the form of aerosols are represented by 8 anatomical groups for use in medicine and 3 anatomical groups for veterinary practice. The market segmentation of these drug groups was carried out in accordance with the classification of ATC, by active substance, producing countries, type of propellant and field of purpose. It was determined that the share of aerosols for inhalation (Antiasthmatics) is 53.85%, aerosols for use in the oral cavity - 15.38%, aerosols for local use - 12.3%. Conclusions. The analysis of the Ukraine pharmaceutical market in pressurised pharmaceutical preparations segment shows an increase in domestic market of pharmaceuticals not only in number of products, but also in medical indications of pharmaceuticals and veterinary drugs. The modern production facilities and the modern technologies of development for the production of metered aerosols remains an important factor in the market growth. A promising direction is the creation of foam drugs in the form of aerosols for the prevention and treatment of gynecological diseases, intrauterine infections and dermatological lesions.

Fast Retrieval Method of Portal Information Based on a Chaotic Genetic Algorithm

The traditional retrieval method cannot respond to the influence of the change in the portal website’s information characteristics, resulting in low efficiency. In this regard, a fast information retrieval method based on a chaotic genetic algorithm is proposed. According to the relevant theory of association rules, the correlation between information data of dynamic portal websites is calculated; different portal website information is retrieved based on the Markov model output; a chaotic genetic algorithm is used to fuse different portal website information. The information data constructs a decision tree for rapid retrieval of portal information, uses the vector form to express the characteristics of portal information, and finally realizes the rapid retrieval of portal information. The experimental results show that the designed method takes up to 15 ms when the sample complexity is high, which shows that the designed method has high efficiency and is of great significance in practical applications.

An Information Retrieval-Based Joint System for Complex Chinese Knowledge Graph Question Answering

Knowledge graph-based question answering is an intelligent approach to deducing the answer to a natural language question from structured knowledge graph information. As one of the mainstream knowledge graph-based question answering approaches, information retrieval-based methods infer the correct answer by constructing and ranking candidate paths, which achieve excellent performance in simple questions but struggle to handle complex questions due to rich entity information and diverse relations. In this paper, we construct a joint system with three subsystems based on the information retrieval methods, where candidate paths can be efficiently generated and ranked, and a new text-matching method is introduced to capture the semantic correlation between questions and candidate paths. Results of the experiment conducted on the China Conference on Knowledge Graph and Semantic Computing 2019 Chinese Knowledge Base Question Answering dataset verify the superiority and efficiency of our approach.

From Text Representation to Financial Market Prediction: A Literature Review

News dissemination in social media causes fluctuations in financial markets. (Scope) Recent advanced methods in deep learning-based natural language processing have shown promising results in financial market analysis. However, understanding how to leverage large amounts of textual data alongside financial market information is important for the investors’ behavior analysis. In this study, we review over 150 publications in the field of behavioral finance that jointly investigated natural language processing (NLP) approaches and a market data analysis for financial decision support. This work differs from other reviews by focusing on applied publications in computer science and artificial intelligence that contributed to a heterogeneous information fusion for the investors’ behavior analysis. (Goal) We study various text representation methods, sentiment analysis, and information retrieval methods from heterogeneous data sources. (Findings) We present current and future research directions in text mining and deep learning for correlation analysis, forecasting, and recommendation systems in financial markets, such as stocks, cryptocurrencies, and Forex (Foreign Exchange Market).

Reliability-driven time series data analysis in multiple-level deep Learning methods utilizing soft computing methods

This paper introduces a novel method for mining data & information retrieval using series data for the duration. A multi-resolution S transformis viewed as a stage-adjusted transform of wavelet/a parameter window low period. Fourier-transform is used to recover significant characteristics from nonstationary duration series data with electricity-network disruptions. In pattern classification of disturbance waveform information, and incorporated Learning Vector Quantization neural-network & different feed-forward neural-network designs were employed afterwardextending required characteristics from the period of series-data.A fuzzy Multilayer perceptron accepts other connectionist systems and therefore is utilized in the final phase of encoding information inside the linking weights to produce fuzzy disturbance rules pattern inference. With energy signal time series data, a pattern classification performance of 99% was attained. Utilizing the new measurement processes, the data-driven information retrieval was displayed. A method proposed in this research is generically used to mine for information similarities in either duration of the series data pattern.

Phytochemical profiling of Piper crocatum and its antifungal activity as Lanosterol 14 alpha demethylase CYP51 inhibitor: a review

Mycoses or fungal infections are a general health problem that often occurs in healthy and immunocompromised people in the community. The development of resistant strains in Fungi and the incidence of azole antibiotic resistance in the Asia Pacific which reached 83% become a critical problem nowadays. To control fungal infections, substances and extracts isolated from natural resources, especially in the form of plants as the main sources of drug molecules today, are needed. Especially from Piperaceae, which have long been used in India, China, and Korea to treat human ailments in traditional medicine. The purpose of this review was to describe antifungal activity from Piper crocatum and its phytochemical profiling against lanosterol 14 alpha demethylase CYP51. The methods used search databases from Google Scholar to find the appropriate databases using Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow diagram as a clinical information retrieval method. From 1,150,000 results search by database, there were 73 selected articles to review. The review shows that P. crocatum contains flavonoids, tannins, terpenes, saponins, polyphenols, eugenol, alkaloids, quinones, chavibetol acetate, glycosides, triterpenoids or steroids, hydroxychavikol, phenolics, glucosides, isoprenoids, and non-protein amino acids. Its antifungal mechanisms in fungal cells occur due to ergosterol especially lanosterol 14 alpha demethylase CYP51 inhibition as a result of 5,6 desaturase (ERG3) downregulation. P. crocatum has an antifungal activity by its phytochemical profiling that act against fungi by inhibiting the fungal cytochrome P 450 pathway, make damaging cell membranes, fungal growth inhibition, morphological changes, and fungal cell lysis.

A Comparative Optimization Model of Japanese Literature Characteristics for Cognitive Retrieval of Cross-Language Information.

Cross-language information cognitive retrieval has grown in importance as a study area due to the multilingual character of Internet resources and the diversifying languages spoken by users. This paper analyses and illustrates the key characteristics of Japanese literature from three aspects: ideological structure, structural form, and emotional expression, and makes a straightforward comparison with Chinese literature using a number of well-known Japanese literary masterpieces as examples. An important aspect of this literature is that it is full of lingering feelings, leaving room for the readers to imagine. The lingering charm is endless, obscure, and meaningful, which is the traditional style of Japanese literature. It also focuses on the depoliticization of Japanese literature, that is, the relative separation between literature and politics. The subjectivity of Japanese writing, which goes beyond just conveying feelings but involves less objective description, is related to this. The process of cross-language information retrieval in foreign nations is primarily separated into three stages, according to the changes in research objects. Currently, adding a language conversion mechanism to a monolingual information retrieval system serves as the primary language information retrieval solution. Currently, nearly 40% of the global popularity of literature comes from Japan. In this article, the background and concept of cross-language information retrieval are introduced, and its types, system models, and several key cross-language information retrieval methods are explained, and some solutions to the factors influencing the cross-language information retrieval effect are suggested.

Analysis of Network Information Retrieval Method Based on Metadata Ontology

In order to solve the problem that people can accurately search for the network information they need, the research on network information retrieval methods becomes more important. This article is mainly about the research of network information retrieval methods based on metadata ontology calculations. This article constructs an LDA three-layer Bayesian model with a three-layer structure of document, topic, and single order. The three-layer structure obeys a random polynomial distribution and can be calculated the joint distribution probability of all variables in the LDA model greatly increases the calculation efficiency. Using a cross-modal information retrieval method, it can mine the common data features between different modal data and analyze the semantic correlation between different modal data, improve the accuracy of search, and solve the existence of different modal data. There is a gap in the expression semantics between heterogeneous and different modal data. The experimental results in this paper show that the text feature extraction of the network information retrieval method based on the metadata ontology calculation has a good performance in terms of accuracy, and the accuracy of the extraction and clustering results is as high as about 90%. The improved CCA algorithm used is better than the traditional CCA and the accuracy is improved by 23%, which is 12% higher than the LDA-CCA algorithm.

Perspective and current state of international standards influence on prevention and treatment of diseases through the prism of developing high-quality and safe medicines

The aim of the work was to examine international legislation covering the legal responsibility in the pharmaceutical field; to analyze activities of international organizations in the area of combating counterfeit drug distribution. Materials and methods. The study was based on international legal instruments aimed at ensuring legal certainty concerning consideration of drug falsification and similar crimes and imposition of criminal liability for activities related to the falsification of medicines. During the work we used methods of information retrieval, analysis, systematization and generalization. Results. An urgent problem of modern medicine is the use of high-quality drugs for the treatment and prevention of diseases. Compliance with standards, in particular international ones, during the development of drugs is the key to both effective treatment and further research in the field of basic clinical medicine, including obtaining new scientific data, improving methods of prevention, diagnosis and treatment of internal diseases. Transformation processes related to Ukraine’s European integration, development of the pharmaceutical industry market, establishment of additional requirements for the quality and safety of medicines necessitate the implementation of state policy aimed at implementing European Union (EU) legal regulations governing legal liability for offenses. committed in pharmaceutical activities. Special attention is paid to the issues of harmonization of the Ukrainian national legislation with the international legislation on the regional level developed by the Council of Europe and the EU on combating the falsification of medicines, including the imposition of criminal liability. Conclusions. The implementation of the international legislation norms of providing legal liability for offenses in pharmaceutical activity into the national legislation plays a positive role, eliminating the shortcomings of the relevant Ukrainian legislative acts.

The least-used key selection method for information retrieval in large-scale Cloud-based service repositories

As the number of devices connected to the Internet of Things (IoT) increases significantly, it leads to an exponential growth in the number of services that need to be processed and stored in the large-scale Cloud-based service repositories. An efficient service indexing model is critical for service retrieval and management of large-scale Cloud-based service repositories. The multilevel index model is the state-of-art service indexing model in recent years to improve service discovery and combination. This paper aims to optimize the model to consider the impact of unequal appearing probability of service retrieval request parameters and service input parameters on service retrieval and service addition operations. The least-used key selection method has been proposed to narrow the search scope of service retrieval and reduce its time. The experimental results show that the proposed least-used key selection method improves the service retrieval efficiency significantly compared with the designated key selection method in the case of the unequal appearing probability of parameters in service retrieval requests under three indexing models.