Information Retrieval Mechanisms Research Articles

Optogenetic stimulation of medial septal glutamatergic neurons modulates theta-gamma coupling in the hippocampus

Hippocampal cross-frequency theta-gamma coupling (TGC) is a basic mechanism for information processing, retrieval, and consolidation of long-term and working memory. While the role of entorhinal afferents in the modulation of hippocampal TGC is widely accepted, the influence of other main input to the hippocampus, from the medial septal area (MSA, the pacemaker of the hippocampal theta rhythm) is poorly understood. Optogenetics allows us to explore how different neuronal populations of septohippocampal circuits control neuronal oscillations in vivo. Rhythmic activation of septal glutamatergic neurons has been shown to drive hippocampal theta oscillations, but the role of these neuronal populations in information processing during theta activation has remained unclear. Here we investigated the influence of phasic activation of MSA glutamatergic neurons expressing channelrhodopsin II on theta-gamma coupling in the hippocampus. During the experiment, local field potentials of MSA and hippocampus of freely behaving mice were modulated by 470 nm light flashes with theta frequency (2–10) Hz. It was shown that both the power and the strength of modulation of gamma rhythm nested on hippocampal theta waves depend on the frequency of stimulation. The modulation of the amplitude of slow gamma rhythm (30–50 Hz) prevailed over modulation of fast gamma (55–100 Hz) during flash trains and the observed effects were specific for theta stimulation of MSA. We discuss the possibility that phasic depolarization of septal glutamatergic neurons controls theta-gamma coupling in the hippocampus and plays a role in memory retrieval and consolidation.

Indexing Excellence: Best Practices in Library Abstracting for Information Retrieval

Indexing and abstracting play pivotal roles in organizing and retrieving information within libraries. This paper delves into the intricate landscape of optimal indexing practices and their profound implications on the efficiency of information retrieval systems. By scrutinizing the methodologies employed in creating high-quality abstracts, this study aims to shed light on their substantial influence on the accessibility and usability of information resources within library collections. The investigation focuses on the multifaceted aspects of indexing excellence, exploring its direct correlation with improved information retrieval mechanisms. It examines in detail the methodologies, standards, and tools utilized in the creation and application of high-quality abstracts. Furthermore, it critically evaluates the utilization of controlled vocabularies and adherence to established indexing guidelines, highlighting their indispensable roles in ensuring the precision and relevance of indexed content. Analyzing the intersection of indexing practices and their impact on information retrieval, this study aims to provide valuable insights into enhancing the efficacy of library abstracting. Recognizing the importance of streamlined access to information resources, this research endeavors to contribute to the continual evolution of information science and user-centered library services.

Applied logic and semantics on indoor and urban adaptive design through knowledge graphs, reasoning and explainable argumentation

Abstract In the previous two decades, knowledge graphs (KGs) have evolved significantly, inspiring developers to build ever-more context-related KGs. Due to this development, artificial intelligence (AI) applications can now access open domain-specific information in a format that is both semantically rich and machine comprehensible. In this article, a framework that depicts functional design for indoor workspaces and urban adaptive design, in order to help architects, artists, and interior designers for the design and construction of an urban or indoor workspace, based on the emotions of human individuals, is introduced. For the creation of online adaptive environments, the framework may incorporate emotional, physiological, visual, and textual measures. Additionally, an information retrieval mechanism that extracts critical information from the framework in order to assist the architects, artists, and the interior designers is presented. The framework provides access to commonsense knowledge about the (re-)design of an urban area and an indoor workspace, by suggesting objects that need to be placed, and other modifications that can be applied to the location, in order to achieve positive emotions. The emotions referred reflect to the emotions experienced by an individual when being in the indoor or urban area, which are pointers for the functionality, the memorability, and the admiration of the location. The framework also performs semantic matching between entities from the web KG ConceptNet, using semantic knowledge from ConceptNet and WordNet, with the ones existing in the KG of the framework. The paper provides a set of predefined SPARQL templates that specifically handle the ontology upon which the knowledge retrieval system is based. The framework has an additional argumentation function that allows users to challenge the knowledge retrieval component findings. In the event that the user prevails in the reasoning, the framework will learn new knowledge.

ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge.

Objective The primary aim of this research was to address the limitations observed in the medical knowledge of prevalent large language models (LLMs) such as ChatGPT, by creating a specialized language model with enhanced accuracy in medical advice. Methods We achieved this by adapting and refining the large language model meta-AI (LLaMA)using a large dataset of 100,000 patient-doctor dialogues sourced from a widely used online medical consultation platform. These conversations were cleaned and anonymized to respect privacy concerns. In addition to the model refinement, we incorporated a self-directed information retrieval mechanism, allowing the model to access and utilize real-time information from online sources like Wikipedia and data from curated offline medical databases. Results The fine-tuning of the model with real-world patient-doctor interactions significantly improved the model's ability to understand patient needs and provide informed advice. By equipping the model with self-directed information retrieval from reliable online and offline sources, we observed substantial improvements in the accuracy of its responses. Conclusion Our proposed ChatDoctor, represents a significant advancement in medical LLMs, demonstrating a significant improvement in understanding patient inquiries and providing accurate advice. Given the high stakes and low error tolerance in the medical field, such enhancements in providing accurate and reliable information are not only beneficial but essential.

A machine learning model for textured X-ray scattering and diffraction image denoising

With the advancements in instrumentations of next-generation synchrotron light sources, methodologies for small-angle X-ray scattering (SAXS)/wide-angle X-ray diffraction (WAXD) experiments have dramatically evolved. Such experiments have developed into dynamic and multiscale in situ characterizations, leaving prolonged exposure time as well as radiation-induced damage a serious concern. However, reduction on exposure time or dose may result in noisier images with a lower signal-to-noise ratio, requiring powerful denoising mechanisms for physical information retrieval. Here, we tackle the problem from an algorithmic perspective by proposing a small yet effective machine-learning model for experimental SAXS/WAXD image denoising, allowing more redundancy for exposure time or dose reduction. Compared with classic models developed for natural image scenarios, our model provides a bespoke denoising solution, demonstrating superior performance on highly textured SAXS/WAXD images. The model is versatile and can be applied to denoising in other synchrotron imaging experiments when data volume and image complexity is concerned.

Preliminary Application of Space-Based Remote Sensing and Geospatial Technology for Investigation on the Geo-Environmental Consequences of Cyclone Aila 2009 in the NW Bangladesh

Climate change phenomena have become a great concern in the context of global change and increased frequency and magnitude of natural disasters throughout the world in the recent time. Natural disasters like floods, cyclones, storm surges etc. often cause significant losses of life, large-scale economic and social impacts, and considerable environmental damages. This paper dealt with study on the overall methodological development to investigate the consequences of climate change extremities particularly, the after effects of cyclone using Remote Sensing (RS) and Geographic Information System (GIS) technology. In this connection, individual functional components have been investigated, tested and verified. Time series multi-sensor satellite data particularly of Landsat, World view (from Google origin) have been utilized to infer information on the consequences of Cyclone Aila 2009 hitting part of southwestern Bangladesh as a test case under the present study. Information retrieval mechanism utilized is based on specially designed methodological framework using satellite-based RS technology along with GIS. Developed technical approach consists of a three-fold components – (i) Establishment of input foundation layer with high spatial details to support characterization, recognition and identification of important surface features using high resolution satellite data; (ii) Effective operational procedure to process, analyze, interpret and finally to archive the retrieved information; (iii) Functional computations were made using spatial modeler language (SML) programming environment under ERDAS Imagine image processing software. Satellite image processing, analysis operation together with image-based spectral characterization of surface features under different stressing conditions etc. have been exercised to derive useful surface information. Specially designed geospatial database has been established in GIS using ArcInfo professional software. Varieties of geospatial data from diverse sources have been incorporated categorically as column-based attributes in GIS. Dual spatial data layers have been generated in GIS for two different dates representing the pre-cyclone and post-cyclone time sequences utilizing appropriate high spatial resolution satellite images. Finally, a geospatial image-based combinational technique has been utilized employing high temporal and moderate spatial resolution time series satellite data with low temporal and high spatial resolution satellite data. Such an operation results in an improved spatial and temporal resolution of output products enabling capture of dynamics of surface features in the spatiotemporal domain providing more precision and details in the output. This study has been supplemented with necessary Ground Position System (GPS)-based ground truthing, selected field data collection and field-based group discussions outcomes.

Implementing a Hybrid Recommendation System to Personalize Customer Experience in E-Commerce Domain

With the rapid use of the internet, a huge amount of data is generated over the network with every passing second. On the other hand, the user demands information that is relevant to his personal search. The processing of such huge data is a challenging task. To serve the user with his specific required information, there is a need for an information retrieval mechanism that can process this large data. A recommendation system is such required technology that retrieves information in order to improve users’ access and thereby recommending items that are relevant to his explicitly mentioned behavior and preferences. The recommendation algorithm analyzes the huge dataset and focuses to recommend accurate content to the user. There are several recommendation systems in use today, some popular among them are Netflix, YouTube, Tinder, and Amazon. In this article, discussion is done on various types of recommendation systems, issues of recommendation systems, and use cases of widely used recommendation engines and their potential benefits. The work introduced in this paper is an integration approach of domain-specific and item-based recommendation system. The performance of the proposed algorithm is measured on evaluation metrics precision and recall. Experimental results prove that the approach introduced in this paper performed well as compared to existing methods.

Web-Based File Clustering and Indexing for Mindoro State University

Purpose – The Web-Based File Clustering and Indexing for Mindoro State University aim to organize data circulated over the Web into groups/collections to facilitate data availability and access, and at the same time meet user preferences. The main benefits include: increasing Web information accessibility, understanding users’ navigation behavior, improving information retrieval and content delivery on the Web. Web-based file clustering could help in reaching the required documents that the user is searching for. Method – In this paper a novel approach has been introduced for search results clustering that is based on the semantics of the retrieved documents rather than the syntax of the terms in those documents. Data clustering was used to improve the information retrieval from the collection of documents. Data were processed and analyzed using SPSS (version 18) where the instrument was evaluated to test the reliability and validity of the measures used. Evaluation was based on a Likert scale of Excellent, Good, Fair, and Poor as described for the selected quality characteristics. Results – A total of 200 questionnaires were distributed with a return rate of 100%. The questionnaire was tested 0.735 using Cronbach’s Alpha Coefficient and considered a reliable instrument. Four quality characteristics were evaluated in this study; Usability, Performance Efficiency, Reliability, and Functionality Suitability. Conclusion - The Web-based file clustering could help in reaching the required documents that the user is searching for. The need for an information retrieval mechanism can only be supported if the document collection is organized into a meaningful structure, which allows part or all the document collection to be browsed at each stage of a search. Recommendations – It is recommended that upon uploading of file it will show the use of the file and where it is originated (department). It is also recommended to create an index to cluster not only the file type but also the content and use of a file. Explore the clustering to a wider scope. Practical Implications – Document clustering provides a structure for organizing large bodies of text for efficient browsing and searching and helps a lot for the Mindoro State University for records/ document processing. Indexing is the best tool to maintain uniqueness of records in a database. Whenever new files or records are created, it can be easily added to the index. This makes it easy to keep documents up-to-date at all times. Grouping documents into two or more categories improves search time and makes life easier for everyone.

Preliminary Application of Space-based Remote Sensing and Geospatial Technology for Investigation on the Geo-environmental Consequences of Cyclone Aila 2009 in the Bangladesh

Climate change phenomena have become a great concern in the context of global change and increased frequency and magnitude of natural disasters throughout the world in the recent time. Natural disasters like floods, cyclones, storm surges etc. often cause significant losses of life, large-scale economic and social impacts, and considerable environmental damages. This paper dealt with study on the overall methodological development to investigate the consequences of climate change extremities particularly, the after effects of cyclone using Remote Sensing (RS) and Geographic Information System (GIS) technology. In this connection, individual functional components have been investigated, tested and verified. Time series multi-sensor satellite data particularly of Landsat, World view (from Google origin) have been utilized to infer information on the consequences of Cyclone Aila 2009 hitting part of southwestern Bangladesh as a test case under the present study. Information retrieval mechanism utilized is based on specially designed methodological framework using satellite-based RS technology along with GIS. Developed technical approach consists of a three-fold components – (i) Establishment of input foundation layer with high spatial details to support characterization, recognition and identification of important surface features using high resolution satellite data; (ii) Effective operational procedure to process, analyze, interpret and finally to archive the retrieved information; (iii) Functional computations were made using spatial modeler language (SML) programming environment under ERDAS Imagine image processing software. Satellite image processing, analysis operation together with image-based spectral characterization of surface features under different stressing conditions etc. have been exercised to derive useful surface information. Specially designed geospatial database has been established in GIS using ArcInfo professional software. Varieties of geospatial data from diverse sources have been incorporated categorically as column-based attributes in GIS. Dual spatial data layers have been generated in GIS for two different dates representing the pre-cyclone and post-cyclone time sequences utilizing appropriate high spatial resolution satellite images. Finally, a geospatial image-based combinational technique has been utilized employing high temporal and moderate spatial resolution time series satellite data with low temporal and high spatial resolution satellite data. Such an operation results in an improved spatial and temporal resolution of output products enabling capture of dynamics of surface features in the spatiotemporal domain providing more precision and details in the output. This study has been supplemented with necessary Ground Position System (GPS)-based ground truthing, selected field data collection and field-based group discussions outcomes.

Carbon to Diamond: An Incident Remediation Assistant System From Site Reliability Engineers’ Conversations in Hybrid Cloud Operations

Conversational channels are changing the landscape of hybrid cloud service management. These channels are becoming important avenues for Site Reliability Engineers (SREs) %Subject Matter Experts (SME) to collaboratively work together to resolve an incident or issue. Identifying segmented conversations and extracting key insights or artefacts from them can help engineers to improve the efficiency of the incident remediation process by using information retrieval mechanisms for similar incidents. However, it has been empirically observed that due to the semi-formal behavior of such conversations (human language) the conversations are very unique in nature and also contain domain-specific terms. %It is important to identify the correct keywords and artefacts like symptoms, issue etc., present in the conversation chats. In this paper, we build a framework that taps into the conversational channels and uses various learning methods to (1) understand and extract key artefacts from conversations like diagnostic steps and resolution actions taken and (2) present an approach to identify past conversations about similar issues. Experimental results on our dataset show the efficacy of the methods used in our proposed system.

Information Retrieval and Sentimental Analysis with Databricks

With the rapid development of cloud computing, the information increases rapidly. Cheap cloud storage and computing power accelerate the development of massive data, and make the large data information collection and knowledge retrieval become necessary. The percentage of unstructured big data is more than 50%, so it is stored in the form of a file for the most part. Big data is split into many blocks that are stored within the server with some corresponding metadata of storage on the master server. How to collect the big data and keywords and retrieve the information are discussed in this paper. The proposed methodology is the information retrieval and sentimental analysis mechanism to improve text information retrieval and opinion mining using databricks. Information retrieval and analysis become more popular research fields within the world. Big data is a collection of heterogeneous structured, unstructured and semi-unstructured data. The basic aim of this paper is to present a broad picture of massive Data and to point out how information are often retrieved using evolutionary computation techniques i.e. databricks that help in the information retrieval process in a better way compared to traditional retrieval techniques. This paper also covered the setup and configuration of databricks.

РАСПРЕДЕЛЕННОЕ ИЗВЛЕЧЕНИЕ ИНФОРМАЦИИ ИЗ КРУПНОМАСШТАБНЫХ БЕСПРОВОДНЫХ СЕНСОРНЫХ СЕТЕЙ

The article discusses distributed information extraction from large-scale wireless sensor networks. The first subsection identifies a family of applications that could benefit from a higher-level information retrieval and delivery mechanism integrated into the WSN, and discusses the state of the art of deployment in a particular application. The following describes the requirements for an integrated high-level information retrieval engine and provides an overview of the prior art in complex queries and processing on a network. The next subsection defines WSN topologies, architectures and protocols suitable for modern information systems; this is followed by a description of a new hybrid approach called a distributed complex query processor.

Ўзбекистон Республикасининг биринчи президенти электрон архивининг яратилиши

The article discusses some aspects of the organization of the information system of the Archive of the First President of the Republic of Uzbekistan Islam Karimov. The problem of digitalization of archival activities is quite relevant for Uzbekistan due to the fact that traditional approaches to the construction of information retrieval mechanisms and the organization of information services in archiving today often do not correspond to the required level of efficiency and user needs. As part of the work carried out, the specific task was to organize a software environment for the formation of an electronic catalog of archived descriptions, filling databases of digitized copies of archival materials, as well as flexible multidimensional search and presentation of information. The way to solve this problem is to use the archival information system «AtoM». As a result, a database of digitized archival materials was created, reflecting the life and work of the First President of the Republic of Uzbekistan Islam Karimov, equipped with a developed search and reference apparatus.

An ontology-based information retrieval mechanism for E-learning management system

During the last decade, increasing attention has been focused on ontologies and Ontological Engineering. The application of ontological engineering technique to e-learning, though still in its early stage, has become a phenomena tool in the integration and adaptation of a dynamic and flexible elearning environment, where tutors and learners are provided with the functionality of personalizing their learning process, this has not fully considered the generality of an entire field of study. This paper presents an ontology-based framework for classification of the entire courses of computer science at the university level. A model of an e-learning domain in the context of material classification that enables students retrieve information about a particular course in personalized ways was presented the method adopted elicited e-learning knowledge using documented materials, observation, consultation, prototyping among others. With domain expertise developed, the knowledge elicited was analyzed and formally represented using OWL-Description Logic. The design was implemented using Protégé 5.0 editor. The validation for accuracy and completeness was carried out with the domain and ontology experts using consistency checking reasoned, DL-Query. The results of the implementation show that elearning materials can be searched and retrieved easily and timely. The study also established and formalised an effective computational approach for representing information content of educational resources thereby helping to improve the flexibility of content representation in an e-learning system.Keywords: e-learning, Ontological Engineering, Ontology, Ontology Web Language (OWL), Knowledge Management, Description LogicVol. 26, No 1, June 2019

Land use discovery based on Volunteer Geographic Information classification

Nowadays, cities are dynamic ecosystems where urban changes occur at a very fast pace. Hence, social sensing has become a powerful tool to uncover the actual land-use of a metropolis. However, current solutions for land-use discovery based on user-generated data usually rely on an information retrieval mechanism applied on a textual corpus. This causes ad-hoc place labelling with limited semantic meaning. In this line, the present work introduces a novel data-driven methodology that extends existing solutions by means of a classifier based on a pre-defined hierarchy of land categories. Two types of social networks –text-based and venue-based platforms– are utilized to train the classifier, which is then applied to infer the use of the land based on text data in areas where venue data are not available. The approach has been evaluated by using large datasets comprising two large cities, showing an accuracy above 90% in predicting the land-use categories.

Noncoding RNAs Databases: Current Status and Trends.

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

A Novel information retrieval mechanism with secure authentication for third party applications in cloud based integration

A Novel information retrieval mechanism with secure authentication for third party applications in cloud based integration

Applications of Nature-Inspired Algorithms in Different Aspects of Semantic Web

Nature has always inspired us all the waggle dance of Honey bee, the school of whales and the swarm of ants, each element when observed carefully has the abundance of teachings. If we carefully observe nature, we find that although Nature seems to be very simple and systematic, it hides many complexities underneath it. As technology also follows the same principle of ‘simple-yet-complex’, the researchers have always tried to apply the learning from Nature to complex technological Algorithms used to solve few real life human problems. Since the past decade, there has been a rapid increase of research in this field. Today Nature Inspired algorithms have permeated into almost all areas of sciences. Although it had been applied to various areas of sciences, the scope of this paper is limited to its application in the domain of The Semantic Web. The main objective of Semantic web applications is to obtain, manage and utilize the huge amount of information that is available in either structured semistructured or unstructured databases in distributed environment. This is an emerging domain and is advancing towards more and more intelligent and human oriented applications. This paper presents a survey of vital nature-Inspired techniques that can be used for optimizing various areas of Semantic web applications such as knowledge base, content filtering, Information Retrieval and Inference mechanism.

A Survey of Information Retrieval Techniques

The explosive growth of resources stored in various forms and transmitted over the internet has necessitated researches into information retrieval technologies. The major information retrieval mechanisms commonly employed include vector space model, Boolean model, Fuzzy Set model, and probabilistic retrieval model. These models are used to find similarities between the query and the documents to retrieve documents that reflect the query. These approaches are based on key-word, which uses lists of keywords to describe the information content. In this paper, a survey of these models is provided in order to understand their working mechanisms and shortcomings. This understanding is vital as it facilitates the choice of an information retrieval technique, based on the underlying requirements. The results of this survey revealed that the current information retrieval models fall short of the expectations in one way or the other. As such, they are not ideal for high precision information retrieval applications.

A Hill-Climbing based Information Retrieval Mechanism for Formal Concept Analysis in Folksonomies

Folksonomies are a means by which communities of people annotate resources with tags. The information contributed by this action can be used to effectively search for an object. A method is required to represent and categorize objects based on their tags and search them effectively by providing words that are relevant to any object. Few existing systems use lattice structure that conceptually groups tagged objects into nodes. The lattice structure facilitates fast search and creates relations between groups of objects. In order to search objects existing statistical and mathematical measures solely take into account a few factors such as the co-occurrence of two tags rather than their meanings to determine their similarity. Hence, when searching for an object based on a user query in a lattice structure there is a need for semantic similarity measure for comparing the meaning of each query with the tags at each lattice node. Also, existing search algorithms like breadth first search process more number of concepts than necessary and so there is a need for a mechanism that performs a narrower search through the lattice. The proposed system represents and categorizes objects with a lattice structure and specifies an optimized search technique based on semantic similarity. Semantic similarity is found by extracting synonyms of the user's query and mapping them to the tags of the object in the lattice structure. In order to achieve an optimized top-down search a novel hill-climbing approach is performed over the lattice that processes minimal number of lattice nodes. Experimental results show the efficiency of the search algorithm along with the similarity measures with near accurate results to the user's query.