Efficient Information Retrieval Research Articles

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.

Pika: Secure Computation using Function Secret Sharing over Rings

Machine learning algorithms crucially depend on non-linear mathematical functions such as division (for normalization), exponentiation (for softmax and sigmoid), tanh (as an activation function), logarithm (for crossentropy loss), and square root (for back-propagation of normalization layers). However, when machine learning is performed over secure computation, these protocols incur a large communication overhead and high round complexity. In this work, we propose new multi-party computation (MPC) protocols for such functions. Our protocols achieve constant round complexity (3 for semi-honest, 4 for malicious), an order of magnitude lower communication (54 − 121× lower than prior art), and high concrete efficiency (2−1163× faster runtime). We rely on recent advances in function secret sharing (FSS) to construct these protocols. Our contributions can be summarized as follows: (1) A constant round protocol to securely evaluate nonlinear functions such as division, exponentiation, logarithm, and tanh (in comparison to prior art which uses round complexity proportional to the rounds of iterative methods/required precision) with high accuracy. This construction largely follows prior work in look-up style secure computation. (2) Our main contribution is the extension of the above protocol to be secure in the presence of malicious adversaries in the honest majority setting. We provide a malicious sketching protocol for FSS schemes that works over rings and in order to prove its security, we extend (and prove) a corresponding form of SchwartzZippel lemma over rings. This is the first such extension of the lemma and it can be of independent interest in other domains of secure computation. (3) We implement our protocol and showcase order of magnitude improvements in runtime and communication. Given the low round complexity and substantially lower communication, our protocols achieve even better performance over network constrained environments such as WAN. Finally, we showcase how such functions can lead to scalability in machine learning. Note that techniques presented are applicable beyond the application of machine learning as the protocols effectively present an efficient 1-out-of-N oblivious transfer or an efficient private information retrieval protocol.

A Comparative Study of Engraved-Digit Data Augmentation by Generative Adversarial Networks

In cases where an efficient information retrieval (IR) system retrieves information from images with engraved digits, as found on medicines, creams, ointments, and gels in squeeze tubes, the system needs to be trained on a large dataset. One of the system applications is to automatically retrieve the expiry date to ascertain the efficacy of the medicine. For expiry dates expressed in engraved digits, it is difficult to collect the digit images. In our study, we evaluated the augmentation performance for a limited, engraved-digit dataset using various generative adversarial networks (GANs). Our study contributes to the choice of an effective GAN for engraved-digit image data augmentation. We conclude that Wasserstein GAN with a gradient norm penalty (WGAN-GP) is a suitable data augmentation technique to address the challenge of producing a large, realistic, but synthetic dataset. Our results show that the stability of WGAN-GP aids in the production of high-quality data with an average Fréchet inception distance (FID) value of 1.5298 across images of 10 digits (0–9) that are nearly indistinguishable from our original dataset.

An increase of inhibition drives the developmental decorrelation of neural activity.

Throughout development, the brain transits from early highly synchronous activity patterns to a mature state with sparse and decorrelated neural activity, yet the mechanisms underlying this process are poorly understood. The developmental transition has important functional consequences, as the latter state is thought to allow for more efficient storage, retrieval, and processing of information. Here, we show that, in the mouse medial prefrontal cortex (mPFC), neural activity during the first two postnatal weeks decorrelates following specific spatial patterns. This process is accompanied by a concomitant tilting of excitation-inhibition (E-I) ratio toward inhibition. Using optogenetic manipulations and neural network modeling, we show that the two phenomena are mechanistically linked, and that a relative increase of inhibition drives the decorrelation of neural activity. Accordingly, in mice mimicking the etiology of neurodevelopmental disorders, subtle alterations in E-I ratio are associated with specific impairments in the correlational structure of spike trains. Finally, capitalizing on EEG data from newborn babies, we show that an analogous developmental transition takes place also in the human brain. Thus, changes in E-I ratio control the (de)correlation of neural activity and, by these means, its developmental imbalance might contribute to the pathogenesis of neurodevelopmental disorders.

АНАЛИЗ ТРЕБОВАНИЙ И РАЗРАБОТКА АЛГОРИТМОВ ИНТЕЛЛЕКТУАЛЬНЫХ СЕРВИСОВ МОНИТОРИНГА

The paper considers the problems of strategic analysis and the choice of directions for the developmentof innovative enterprises in the conditions of transition to the 6th technological order and industry4.0. The main levels of analysis are determined. The objectives of the strategic analysis are outlinedbased on the scale of the research being conducted. The analysis tasks are highlighted, the solution ofwhich will allow achieving the set goals. The complexity of solving global monitoring tasks, which arecaused by a large volume of heterogeneous and unstructured information, is shown. In these conditions,thematic search and analytical processing of information cannot be performed without the use of automated information and analytical systems and the creation of search services based on artificial intelligence.A general monitoring procedure is proposed. The main stages of monitoring technological trendsare defined, the tasks to be solved within a specific stage and the planned result are shown. Based on thegeneral monitoring procedure, the main priority functions that the developed services should have aredetermined. As well as the problems of their development and structuring of the received information inthe form of information objects and clustering of documents. In contrast to the well-known global monitoringsystems, in which the search is based on indicators: an increase in the use of keywords, an increasein the number of new authors, quoting works from related fields. Algorithms are proposed thatprovide the definition of reference topics, assessment of ranking and relevance of information. The descriptionof the algorithms is given on the example of creating a summary information table, with thehelp of which the interrelationships of documents of scientific and technological development in eachdirection of monitoring and the search for specific documents in the database are formed. The constructionof search services based on the presented algorithms will ensure the allocation of reference topicsof documents, provide more reliable results of clustering of unstructured information and the formationof scientific and technological trends in information and analytical complexes. To implement the algorithm,it is proposed to use the Python programming language. The implementation of these algorithmswill improve the quality and efficiency of information retrieval in conditions of a large volume of unstructuredinformation.

Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval.

Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques.

Research on Information Retrieval Effectiveness of University Scientific Researchers Based on Mental Model

The information retrieval behavior of scientific researchers is a behavior that is affected by multiple factors such as cognition, emotion, task, and user type and has its unique cognitive characteristics. In the context of scientific research, this paper conducts research on the information retrieval behavior of scientific researchers and analyzes the three levels of user mental model “user cognition, user behavior, and user emotion.” According to the mental model construction process, define the user segmentation of basic tasks, formulate recruitment plan, set interview scope, analyze data, construct mental model, product adjustment and gap analysis, export structure seven steps, construct the mental model of scientific research workers’ information retrieval, carry out research on the information retrieval efficiency of scientific researchers, describe the user portrait based on the mental model, and put forward strategies to improve the efficiency of user information retrieval.

Intelligent Big Information Retrieval of Smart Library Based on Graph Neural Network (GNN) Algorithm.

In order to provide users with more humanized and intelligent big data knowledge services, a research method of intelligent big information retrieval of Smart Library Based on graph neural network (GNN) algorithm is proposed. Through the key technical problems and solutions of information recommendation represented by graph neural network (GNN) algorithm, this method explores how the library can realize the management and value mining of big data knowledge services. The research shows that the intelligent information retrieval of Smart Library Based on graph neural network (GNN) algorithm is 80% higher than the previous general methods. Graph neural network is a more advantageous algorithm for node classification, link prediction, node clustering, or network visualization, which is of great help to improve the efficiency of information retrieval.

Programming of micro/nano‐scale information on low switching temperature shape memory film

AbstractShape memory polymer films have been of great interests in storing functional information because of their optical and mechanical variability. High molecular weight polyvinyl acetate (PVAc) is an ideal shape memory material to program micro‐ and nano‐scale patterns on the surface of highly transparent films. By blending PVAc with polyethylene glycol (PEG) plasticizer, lower switching temperatures could be achieved. We develop a shape memory PVAc/PEG blend film system featuring a unique design by taking advantage of multiple switching temperatures. The effect of the PEG composition was studied relative to different surface patterns, switching temperatures and efficiency of information retrieval by utilizing differential scanning calorimetry and UV/Vis analysis. The results indicate that higher contents of PEG enhanced the recovery efficiency of both the micro‐scale and nano‐scale patterns and the corresponding optical properties. Furthermore, a dual switching shape memory film was developed enabling multiple switching temperatures that can lead to versatile applications.

Accurate and prompt answering framework based on customer reviews and question-answer pairs

As e-commerce markets have gradually expanded, online shopping malls have provided various services aiming to secure competitiveness. A service for providing an accurate and prompt response when a customer writes an inquiry regarding a product represents a space directly connected to the customer and plays an important role, as it is directly related to product sales. However, the current online shopping mall answering service has disadvantages, e.g., it takes time for an administrator to write an answer directly, or to provide an answer within a set of answers. In this paper, we propose an answer framework for solving this problem, based on customer reviews. When a user writes a query, the framework provides an appropriate answer in real time through the system’s question-and-answer pairs and customer reviews. The framework’s performance is verified through a qualitative evaluation. In addition, it is confirmed that a customized model for reflecting the characteristics of each shopping mall can be created by using additional information from the collected data. The proposed framework is expected to support customers’ online shopping through more reliable and efficient information retrieval, and to reduce shopping mall operation and maintenance costs.

Attention Based Encoder Architecture for Automatic Text Classification: A Case Study on Text-news Classification

Text classification is a classical problem of natural language processing where given texts are classified into relevant classes. Text classification techniques are extensively used in finding news categories, search engine optimization, automated textual response and many more. With the exponential growth of digital data, it is inevitable that an automatic classification of documents is needed for efficient information retrieval and document archival. Over the years, text classification problems are approached by using classical machine learning techniques like naive bayes(NB), support vector machine(SVM), logistic regression(LR), random forest(RF) and others. In this article, an Attention based deep learning (DL) model is proposed and applied to the news corpus and then the results are compared with the results of classical machine learning models. A comparative analysis of classification accuracy of the DL model with prominent models like LR, RF and NB are presented in this work.

Application of Convolution Neural Networks in Web Search Log Mining for Effective Web Document Clustering

The volume of web search data stored in search engine log is increasing and has become big search log data. The web search log has been the source of data for mining based on web document clustering techniques to improve the efficiency and effectiveness of information retrieval. In this paper Deep Learning Model Convolution Neural Network(CNN) is used in big web search log data mining to learn the semantic representation of a document. These semantic documents vectors are clustered using K-means to group relevant documents for effective web document clustering. Experiment was done on the data set of web search query and associated clicked URLs to measure the quality of clusters based on document semantic representation using Deep learning model CNN. The clusters analysis was performed based on WCSS(the sum of squared distances of documents samples to their closest cluster center) and decrease in the WCSS in comparison to TF.IDF keyword based clusters confirm the effectiveness of CNN in web search log mining for effective web document clustering.

Unsupervised emotional state recognition based on clustering of EEG features

Efficient information retrieval from the EEG sensors is a complex and challenging task, particularly in the context of psychology, including emotional states. Therefore, different machine learning strategies are considered to improve the processes based on EEG signal analysis. Most of them use supervised approaches since EEG datasets usually include metadata and descriptions that can be used for learning. However, these descriptions are mainly based on self-reports of emotional states, which means that they may not be reliable or objective. The paper proposes an approach that incorporates unsupervised learning techniques as a solution supporting classification where classification labels may be uncertain. The research proved that our approach improves the recognition of emotions and gives results with an average accuracy greater by fve percentage points.

An efficient retrieval of relational database information based on knowledge graph

An efficient retrieval of relational database information based on knowledge graph

A study to rationalize the health-care (clinical and medical) record forms in use at SGPGIMS hospital, in conformance with inter/national guidelines, so as to ensure structural and contextual uniformity while updating the same for prospective in-house consumption by the end users

Background: Effectual management of medical records (MR) is a prime component in health-care delivery organizations. A standardized medical form facilitates for obtaining comprehensive information along with appropriate documentation for swift and efficient retrieval of information pertaining to the quality of patient care. It seems astounding that hitherto, there have been no set standards/criteria to evaluate MR forms. With this backdrop, the study aimed to rationalize MR forms in use at SGPGIMS in conformance with the inter/national guidelines as a stride toward quality improvement.Aim: The aim of this study was to rationalize the health-care (clinical and medical) record forms in use at SGPGIMS hospital, in conformance with inter/national guidelines, to ensure structural and contextual uniformity while updating the same for prospective in-house consumption by the end user.Objectives: The objective of this study was to obtain the health-care (clinical and medical) record forms in use at SGPGIMS hospital. To compare the health-care (clinical and medical) record forms in use at SGPGIMS hospital with inter/national guidelines through the appropriate designed tool, i.e., (structured checklist). To recommend an evidence-based modification in health-care (clinical and medical) record forms based on analysis of data obtained, to ensure structural and contextual uniformity while updating the same for the prospective in-house users, if required.Methodology: A descriptive study was carried out at Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, with a study sample of preidentified health-care (clinical and medical) forms which are the core forms to all the departments of SGPGIMS, hospital. A customized validated tool kit (structured checklist) was designed in conformance with inter/national guidelines to assess the preidentified health-care (clinical and medical) forms.Results: The Medical Certificate of Cause of Death Form was found to have the highest compliance rate of 90.5% with the given set of parameters among the preidentified forms, while the Medication Chart and Assessment Form have noncompliance rates of 61.1% and 58.1%, respectively, with the given set of parameters. Rest other preidentified medical forms have the noncompliance rate of <50% with the given set of parameters of customized tool kit.Conclusion: Since this was the foremost study conducted, it was observed that there was a need to rationalize the MR forms by implementing the observations made, so as to ensure structural and contextual uniformity by the end users.

EFFICIENCY INCREASING OF INFORMATION SEARCH ON A GIVEN TOPIC ON THE INTERNET

The article discusses the ways to overcome the difficulties associated with surfing the Internet for certain information. Many users are not able to explicitly make up a search request according to their information needs, and their search is often limited to haphazard use of several most popular search engines. Most search engine research is technical. At the same time, in recent years more and more researchers have paid the attention to the study of information search issues in the global network. These studies are at the junction of computer science and psychology. It focuses on information behavior and user needs, cognitive factors and information practices of users. The article examines the previously applied conceptual models of information retrieval, formulates a conceptual model of information retrieval on a given topic. The main properties of user interaction with search engines and factors that should be taken into account are considered. The procedure for information search and analysis in universal and scientific search systems is described. The description of experimental testing of the proposed conceptual search model is provided. The article proposes a new conceptual model of search in universal and scientific search engines. This model contributes to the formation of a metacognitive search strategy. In addition, a generalized indicator of the efficiency of information retrieval using such a model is proposed. The proposed conceptual search model can be applied in the educational process. In particular, the article provides an example of students’ use of the elements of the formed metacognitive strategy when doing Bachelor degree paper research.

Development of an Internet-Based Information/DataResource Platform during the COVID-19 Pandemic Using Hierarchical Clustering

There is heavy usage of Internet-based information resource platformsdue to the COVID-19 pandemic. If it can present the search results as a list of groupsof related terms and context, that would be ideal. An additional enhancement would be to display information in the search results in a classified manner. Therefore, the research is proposing an effective and efficient information retrieval system. The methodology adopted follows the conventional Information Retrieval (IR) system design except that Cosine similarity and hierarchical clustering were introduced for search result ranking. The Average Precision of the proposed system was compared with that of flat clustering such as K-means. The result of the evaluation shows that hierarchical clustering improved in precision, being10% better than flat clustering result retrieval result. This led to greater usability, userexperience,and efficiency of the system

Development of an Internet-Based Information/DataResource Platform during the COVID-19 Pandemic Using Hierarchical Clustering

there is heavy usage of Internet-based information resource platformsdue to the COVID-19 pandemic. If it can present the search results as a list of groupsof related terms and context, that would be ideal. An additional enhancement would be to display information in the search results in a classified manner. Therefore, the research is proposing an effective and efficient information retrieval system. The methodology adopted follows the conventional Information Retrieval (IR) system design except that Cosine similarity and hierarchical clustering were introduced for search result ranking. The Average Precision of the proposed system was compared with that of flat clustering such as K-means. The result of the evaluation shows that hierarchical clustering improved in precision, being10% better than flat clustering result retrieval result. This led to greater usability, userexperience,and efficiency of the system.

Different approaches to collaborative problem solving between successful versus less successful problem solvers: Tracking changes of knowledge structure

STEM Problem solving necessitates a substantial amount of specialized domain knowledge. An important element of problem-solving within the domain includes how knowledge is structured and organized in memory to facilitate efficient retrieval of relevant information and future problem solving. In previous studies, however, problem solving and knowledge structure have been studied in relative isolation, resulting in viewing them as a separate two-way process. To address this gap, this study aimed to track how individuals developed their knowledge structures before, during, and after collaborative problem solving in the contexts of STEM (physics, astronomy, and biology), with particular attention to understanding the different mechanism between success versus less successful problem-solvers. For that, we employed a relatively new and promising network approach to representing learners’ knowledge structures as network graphs for analysis and comparison. Results visually demonstrated that successful problem-solvers tend to share a solution-focused knowledge and establish their group knowledge-oriented knowledge structure, whereas the less successful problem-solvers tend to share problem-focused knowledge and then establish their prior knowledge-oriented knowledge structure. Implications and discussion for the findings are provided.

Hyperspectral imaging via a multiplexing digital micromirror device

Spectral imaging technique, which retrieves both spatial and spectral information of a scene or an object, is an important tool in applications such as food inspection, vegetation monitoring and geographic remote sensing. Most spectral imaging systems have a complex system architecture, in which different components are used for different functions, limiting the efficiency of information retrieval. Here, we propose a hyperspectral imaging system with a simple architecture, where a multiplexing digital micromirror device functions both as a spatial light modulator for spatial imaging and a diffractive grating for spectral measuring simultaneously. The proof-of-principle system contains a group of lenses, a digital micromirror device and a one-dimensional detector array, and is capable of performing 128 × 128 pixel resolution hyperspectral imaging over the visible spectrum with an experimental 3.73 nm spectral resolution. Furthermore, by using a compressive sampling strategy, continuous real-time spectral video is performed with a frame-rate up to 10 Hz. The concise architecture of the proposed system, with the potential to be easily extended to both ultraviolet and infrared, offers an alternative scheme to manufacture low-cost, compact, hyperspectral imagers.