Optimal Information Retrieval Research Articles

Optimal intelligent information retrieval and reliable storage scheme for cloud environment and E-learning big data analytics

Optimal intelligent information retrieval and reliable storage scheme for cloud environment and E-learning big data analytics

Unleashing ChatGPT's Power: A Case Study on Optimizing Information Retrieval in Flipped Classrooms via Prompt Engineering

Unleashing ChatGPT's Power: A Case Study on Optimizing Information Retrieval in Flipped Classrooms via Prompt Engineering

Black hole information recovery from gravitational waves

We study the classical and quantum black hole information in gravitational waves from a black hole’s history. We review the necessary concepts regarding quantum information in many-body systems to motivate information retrieval and content in gravitational waves. We then show the first step in an optimal information retrieval strategy is to search for information in gravitational waves, compared to searching for correlations in Hawking radiation. We argue a large portion of the information of the initial collapsing state may be in the gravitational waves. Using the Zerilli equation for particles falling radially into Schwarzschild black holes, we then describe a method to retrieve full classical information about infalling sources, including masses, infall times and angles.

An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval

An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval

O47 Chat with Maya: Assessing the Usability of the Texas WIC Chatbot

<h3>Background</h3> To address declining participation rates in the Women, Infants, and Children (WIC) program, Texas WIC developed and deployed Maya, the Texas WIC chatbot, in June 2020. Maya is an automated software application that allows visitors of TexasWIC.org to ask questions about eligibility and begin the process of making an appointment. Maya has been developed within a user-centered design framework, with WIC clients, potential WIC clients, and WIC staff consulted throughout the ideation and design process. In continuation of this process, usability studies are ongoing. <h3>Objective</h3> The objectives of this study were to assess the usability of Maya and solicit user feedback on overall satisfaction. <h3>Study Design, Setting, Participants</h3> A Think Aloud interview methodology was employed to assess participants' real-time cognitive processes when using Maya. Participants (n = 9) were recruited after accessing Maya on TexasWIC.org. Interviews were conducted via Zoom with a moderator and assistant moderator. First, participants explored Maya's features with minimal input from the interviewer. Next, utilizing a semi-structured interview guide, the interviewer probed for participants' perceptions regarding how Maya may facilitate convenient information retrieval for WIC clients. <h3>Measurable Outcomes/Analysis</h3> Employing a 2-coder structure, interviews were recorded, transcribed, and themed. <h3>Results</h3> Maya's ability to immediately provide critical information about benefits, enrollment, recertification, and WIC-related frequently asked questions was described as efficient and highly useful. Additionally, participants approved of Maya's overall layout and expressed confidence in their ability to find what they needed. The few issues that were uncovered include minor confusion on how to return to Maya's main menu, some chat responses seeming long, and occasional glitches. <h3>Conclusion</h3> Maya was well-received with specific appreciation for her ability to efficiently provide information. However, some issues related to functionality indicate room for improvement. Enhancing Maya's usability to ensure easy usage has the potential further facilitate optimal information retrieval and program participation.

RETRACTED ARTICLE: A Sly Salvage of Semantic Web Content with Insistence of Low Precision and Low Recall

Semantic Web content extracting are the augmentation of the present web where the data is given in the better importance and allowing users to work close by close by utilizing the assets of web. Due to expanding of web content at speedier rate, it is difficult to adapt up to new procedures of separating to create the exact data in light of the user query. Later, number of scientists has taking a shot at enhancing the aftereffects of Web content mining by abusing Web Semantic Structure and can be executed in the web expresses through machine-processable data to bolsters the errands of users. But these methods bring about low precision [unseemly data from different inquiry result (equivocal)] and getting of off base data (i.e. low recall) with high surfing time. Therefore, the paper proposes the Semantic Web content mining utilizing two ways to deal with dodge low precision and low recall: the one is Kalman–Bucy filters, to channel the comparable information; the next is adaptive Helmholtz machine, to get the exact data in light of the user query. The proposed Kalman–Bucy filtering approach with adaptive Helmholtz machine execution is be assessed and contrasted with existing methodologies regarding retrieval accuracy and surfing time by dual predicted output and the optimized information retrieval rather than probability.

MetaNIRS: A Relational Database for the Categorization, Organization and Meta-Analysis of Optical Functional Neuroimaging Research

MetaNIRS: A Relational Database for the Categorization, Organization and Meta-Analysis of Optical Functional Neuroimaging Research

Learning Ranking Functions For Information Retrieval Using Layered Multi-Population Genetic Programming

Ranking plays a key role in many applications, such as document retrieval, recommendation, question answering, and machine translation. In practice, a ranking function (or model) is exploited to determine the rank-order relations between objects, with respect to a particular criterion. In this paper, a layered multipopulation genetic programming based method, known as RankMGP, is proposed to learn ranking functions for document retrieval by incorporating various types of retrieval models into a singular one with high effectiveness. RankMGP represents a potential solution (i.e., a ranking function) as an individual in a population of genetic programming and aims to directly optimize information retrieval evaluation measures in the evolution process. Overall, RankMGP consists of a set of layers and a sequential workflow running through the layers. In one layer, multiple populations evolve independently to generate a set of the best individuals. When the evolution process is completed, a new training dataset is created using the best individuals and the input training set of the layer. Then, the populations in the next layer evolve with the new training dataset. In the final layer, the best individual is obtained as the output ranking function. The proposed method is evaluated using the LETOR datasets and is found to be superior to classical information retrieval models, such as Okapi BM25. It is also statistically competitive with the state-of-the-art methods, including Ranking SVM, ListNet, AdaRank and RankBoost.

Applying Genetic Algorithms to Information Retrieval Using Vector Space Model

Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users' needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms. �

APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL

Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.

HiRes—a tool for comprehensive assessment and interpretation of metabolomic data

The increasing role of metabolomics in system biology is driving the development of tools for comprehensive analysis of high-resolution NMR spectral datasets. This task is quite challenging since unlike the datasets resulting from other 'omics', a substantial preprocessing of the data is needed to allow successful identification of spectral patterns associated with relevant biological variability. HiRes is a unique stand-alone software tool that combines standard NMR spectral processing functionalities with techniques for multi-spectral dataset analysis, such as principal component analysis and non-negative matrix factorization. In addition, HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. Integration of these components together with multivariate analytical procedures makes HiRes very capable of addressing the challenges for assessment and interpretation of large metabolomic datasets, greatly simplifying this otherwise lengthy and difficult process and assuring optimal information retrieval. HiRes is freely available for research purposes at http://hatch.cpmc.columbia.edu/highresmrs.html

Enhancing the MeSH thesaurus to retrieve French online health resources in a quality‐controlled gateway

The amount of health information available on the Internet is considerable. In this context, several health gateways have been developed. Among them, CISMeF (Catalogue and Index of Health Resources in French) was designed to catalogue and index health resources in French. The goal of this article is to describe the various enhancements to the MeSH thesaurus developed by the CISMeF team to adapt this terminology to the broader field of health Internet resources instead of scientific articles for the medline bibliographic database. CISMeF uses two standard tools for organizing information: the MeSH thesaurus and several metadata element sets, in particular the Dublin Core metadata format. The heterogeneity of Internet health resources led the CISMeF team to enhance the MeSH thesaurus with the introduction of two new concepts, respectively, resource types and metaterms. CISMeF resource types are a generalization of the publication types of medline. A resource type describes the nature of the resource and MeSH keyword/qualifier pairs describe the subject of the resource. A metaterm is generally a medical specialty or a biological science, which has semantic links with one or more MeSH keywords, qualifiers and resource types. The CISMeF terminology is exploited for several tasks: resource indexing performed manually, resource categorization performed automatically, visualization and navigation through the concept hierarchies and information retrieval using the Doc'CISMeF search engine. The CISMeF health gateway uses several MeSH thesaurus enhancements to optimize information retrieval, hierarchy navigation and automatic indexing.

Modeling a Multi-Agent System for Retrieving Information from Distributed Sources

This paper presents a multi-agent system called ISAME and designed for intelligent information retrieval from heterogeneous distributed sources. ISAME constitutes a virtual library that supplies a set of software agents with a simplified access to a set of dynamic information sources available under electronic formats, as well as services for facilitating and optimizing information retrieval. The system also uses TCP/IP communication protocols, and proposes a series of garbage-collection mechanisms to avoid the preservation and propagation of information among agents, or messages that become inaccessible or outdated as well as the use of the resources that become undesirable. The originality of this system rests on the fact that it simplifies the information retrieval from distributed heterogeneous sources by making these sources transparent to the user.

The granularity of medical narratives and its effect on the speed and completeness of information retrieval.

Using electronic rather than paper-based record systems improves clinicians' information retrieval from patient narratives. However, few studies address how data should be organized for this purpose. Information retrieval from clinical narratives containing free text involves two steps: searching for a labeled segment and reading its content. The authors hypothesized that physicians can retrieve information better when clinical narratives are divided into many small, labeled segments ("high granularity"). The study tested the ability of 24 internists and 12 residents at a teaching hospital to retrieve information from an electronic medical record--in terms of speed and completeness--when using different granularities of clinical narratives. Participants solved, without time pressure, predefined problems concerning three voluminous, inpatient case records. To mitigate confounding factors, participants were randomly allocated to a sequence that was balanced by patient case and learning effect. Compared with retrieval from undivided notes, information retrieval from problem-partitioned notes was 22 percent faster (statistically significant), whereas retrieval from notes divided into organ systems was only 11 percent faster (not statistically significant). Subdividing segments beyond organ systems was 13 percent slower (statistically significant) than not subdividing. Granularity of medical narratives affected the speed but not the completeness of information retrieval. Dividing voluminous free-text clinical narratives into labeled segments makes patient-related information retrieval easier. However, too much subdivision slows retrieval. Study results suggest that a coarser granularity is required for optimal information retrieval than for structured data entry. Validation of these conclusions in real-life clinical practice is recommended.

The RIAO 94 conference and the status of information retrieval

The RIAO 94 Conference provided a strong collection of presentations giving an excellent panorama of the best in current IR R&amp;amp;D. This review briefly summarizes most of those presentations as a basis for characterizing the current state of research and development in information retrieval. The theme of this characterization is that a great number of exciting developments are bubbling just below (and even into) the operational system status but that these developments could be accelerated by an attention to a number of verities. For example, there are multiple paradigms for improving current operational systems and these need to be carefully evaluated through objective scientific experimentation and analysis so that the best techniques from each can be properly integrated into new, improved systems which will go beyond any one paradigm. Not only must current researchers be aware of competing new paradigms but they need to be aware of the work of past generations in Information Science. In particular, the standard structured, contextual, interactive Boolean techniques of modern retrieval systems do work well under intelligent human control and new attempts to add intelligent computer techniques and aides to this paradigm possibly serve as a better basis for optimized information retrieval than the competing paradigms based on statistics, advanced linguistics, or thesaural/classification development and browsing.

Optimal information retrieval when queries are not random

We consider the complexity of the general information retrieval system design problem and multiattribute file systems based upon the multiple key hashing (MKH) design problem. We first show that the problem of designing an optimal multiattribute file system is NP-hard. The performance formula for multiattribute file systems based upon the MKH method is derived. We also show that the design problem for a multiattribute file system based upon the MKH method is related to the prime number problem. We show that the problem of designing optimal multiattribute files based upon the MKH method can be reduced to finding minimal N-tuples, which was discussed by Chang, Lee and Du. We further present a very efficient method for designing good multiple key hashing functions in the case where the number of buckets is a power of a prime number. We also propose a heuristic algorithm to design good multiple key hashing functions in general.

A comparison of search term weighting

The term relevance weighting method has been shown to produce optimal information retrieval queries under well-defined conditions. The parameters needed to generate the term relevance factors cannot unfortunately be estimated accurately in practice; futhermore, in realistic test situations, it appears difficult to obtain improved retrieval results using the term relevance weights over much simpler term weighting systems such as, for example, the inverse document frequency weights.It is shown in this study that the inverse document frequency weights and the term relevance weights are closely related over a wide range of the frequency spectrum. Methods are introduced for estimating the term relevance weights, and experimental results are given comparing the inverse document frequency with the estimated term relevance weights.

THE ESTIMATION OF TERM RELEVANCE WEIGHTS USING RELEVANCE FEEDBACK

The term relevance weighting method has been shown to produce optimal information retrieval queries under well‐defined conditions. Unfortunately, the relevance weights cannot be determined in the absence of accurate knowledge of the occurrence frequencies of the terms in the relevant and non‐relevant documents of a collection. This study presents a realistic method for estimating the term relevance weights from information derived in an interactive search environment where relevance assessments for previously retrieved items are used later to construct improved query statements. Procedures are introduced for constructing the initial query weights by using estimated term relevance factors. These initial weights are then modified during the relevance feedback process by utilizing the occurrence frequencies of the terms in the retrieved documents obtained from an earlier search. The procedures used to construct the term relevance weights are covered in detail, and experimental output is included to illustrate the effectiveness of the methods.

Optimization of stereoscopic and high angle tilting procedures for biological thin sections.

SUMMARYThe use of stereoscopic and high angle tilting facilities in electron microscopes to optimize information retrieval from thin sections of biological material is reviewed. Some of the technical problems involved are analysed in detail and problems of interpretation of stereo‐pairs and the necessity of ‘learning to see depth’ in such unfamiliar visual situations is emphasized. A table suggesting methods of optimizing various parameters is presented. An indication of some of the unanticipated results which have arisen from the work and of the consequent reservations about currently accepted routine evaluations of biological sections, especially as regards their thickness, are considered. The validity of serial section reconstruction at the 10–50 nm level needs careful consideration.

Telemetry Application in the Glass Industry

Telemetry technique and presently available equipment may be utilized to optimize information retrieval speed and to minimize the total cost of information. An example of equipment is presented with basic theory of operation. Specific applications are discussed in the areas of pressure, temperature, and strain measurement. Technique is reviewed in terms of antenna placement and heat shielding of the telemetry package. By use of the systems approach to information retrieval, telemetry technique can be of value to the glass industry where other methods of measurement are not optimum or applicable.