Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of dat...Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of data-driven operation management,intelligent analysis,and mining is urgently required.To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation,maintenance experience,and knowledge by rule and line,a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology.Based on the processing flow of the operating data of the power distribution system,a technical framework of neural information retrieval is established.Combined with the natural graph characteristics of the power distribution system,a unified graph data structure and a data fusion method of data access,data complement,and multi-source data are constructed.Further,a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed.The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set.The model is verified on the operating section of the power distribution system of a provincial grid area.The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.展开更多
The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information...The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.展开更多
Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers.Familiarizing ontology as information retrieval(IR)aids in augmenting the searching effects of user-req...Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers.Familiarizing ontology as information retrieval(IR)aids in augmenting the searching effects of user-required relevant information.The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet,mapping the connection between keywords and information,and categorizing the retrieval outcomes.The prevailing procedures for IR consume considerable time,and they could not recover information proficiently.In this study,through applying a modified neuro-fuzzy algorithm(MNFA),the IR time is mitigated,and the retrieval accuracy is enhanced for trouncing the above-stated downsides.The proposed method encompasses three phases:i)development of a crop ontology,ii)implementation of the IR system,and iii)processing of user query.In the initial phase,a crop ontology is developed and evaluated by gathering crop information.In the next phase,a hash tree is constructed using closed frequent patterns(CFPs),and MNFA is used to train the database.In the last phase,for a specified user query,CFP is calculated,and similarity assessment results are retrieved using the database.The performance of the proposed system is measured and compared with that of existing techniques.Experimental results demonstrate that the proposed MNFA has an accuracy of 92.77% for simple queries and 91.45% for complex queries.展开更多
With the development and progress of today’s network information technology,a variety of large-scale network databases have emerged with the situation,such as Baidu Library and Weipu Database,the number of documents ...With the development and progress of today’s network information technology,a variety of large-scale network databases have emerged with the situation,such as Baidu Library and Weipu Database,the number of documents in the inventory has reached nearly one million.So how do you quickly and effectively retrieve the information you want in such a huge database?This requires finding efficient algorithms to reduce the computational complexity of the computer during Information Retrieval,improve retrieval efficiency,and adapt to the rapid expansion of document data.The Quicksort Algorithm gives different weights to each position of the document,and multiplies the weight of each position with the number of matches of that position,and then adds all the multiplied sums to set a feature value for Quicksort,which can achieve the full accuracy of Information Retrieval.Therefore,the purpose of this paper is to use the quick sort algorithm to increase the speed of Information Retrieval,and to use the position weighting algorithm to improve the matching quality of Information Retrieval,so as to achieve the overall effect of improving the efficiency of Information Retrieval.展开更多
Objective: Information visualization is the study of interactive depictions of abstract and data to strengthen the human cognition. Designing an appropriate information visualization system may be very useful techniqu...Objective: Information visualization is the study of interactive depictions of abstract and data to strengthen the human cognition. Designing an appropriate information visualization system may be very useful technique for scholars, who intent to get scientific information from digital libraries. The objective of current study was to map and visualize the key-information of dissertations in academic libraries. To achieve the aim, an information retrieval system was designed to present the interactive graphic view of dissertations’ subjects in academic. Methods: An information retrieval system was designed by information visualization toolkit that presents the related subjects of dissertations in academic libraries. In addition, the satisfaction-levels of library-users were analyzed by administrating a standard questionnaire (QUIS Questionnaire). Results: The study indicated that the designed IR system helped to provide a user-friendly environment through displaying subjective relations of dissertations, overwhelming variety of colors in displaying information. Fast and easy access to the cover-to-cover information of dissertations and user-interaction facilities are the advantages of designed IR. Analysis of data furthermore indicated that the users’ satisfaction from the system was from medium to high grade. Conclusion: Designing the IR-system revealed an excessive influence on users’ satisfaction;therefore, proposing such systems for employing in academic libraries is very suitable and its implementation is necessary.展开更多
During a two day strategic workshop in February 2018,22 information retrieval researchers met to discuss the future challenges and opportunities within the field.The outcome is a list of potential research directions,...During a two day strategic workshop in February 2018,22 information retrieval researchers met to discuss the future challenges and opportunities within the field.The outcome is a list of potential research directions,project ideas,and challenges.This report describes the major conclusions we have obtained during the workshop.A key result is that we need to open our mind to embrace a broader IR field by rethink the definition of information,retrieval,user,system,and evaluation of IR.By providing detailed discussions on these topics,this report is expected to inspire our IR researchers in both academia and industry,and help the future growth of the IR research community.展开更多
Neural attention-based encoders,which effectively attend sentence tokens to their associated context without being restricted by long-term distance or dependency,have demonstrated outstanding performance in embedding ...Neural attention-based encoders,which effectively attend sentence tokens to their associated context without being restricted by long-term distance or dependency,have demonstrated outstanding performance in embedding sentences into meaningful representations(embeddings).The Universal Sentence Encoder(USE)is one of the most well-recognized deep neural network(DNN)based solutions,which is facilitated with an attention-driven transformer architecture and has been pre-trained on a large number of sentences from the Internet.Besides the fact that USE has been widely used in many downstream applications,including information retrieval(IR),interpreting its complicated internal working mechanism remains challenging.In this work,we present a visual analytics solution towards addressing this challenge.Specifically,focused on semantics and syntactics(concepts and relations)that are critical to domain clinical IR,we designed and developed a visual analytics system,i.e.,USEVis.The system investigates the power of USE in effectively extracting sentences’semantics and syntactics through exploring and interpreting how linguistic properties are captured by attentions.Furthermore,by thoroughly examining and comparing the inherent patterns of these attentions,we are able to exploit attentions to retrieve sentences/documents that have similar semantics or are closely related to a given clinical problem in IR.By collaborating with domain experts,we demonstrate use cases with inspiring findings to validate the contribution of our work and the effectiveness of our system.展开更多
This paper describes a project that has created a Topic Map search tool for a mathematics educational database containing articles from the journal For the Learning of Mathematics.The resulting website enables users t...This paper describes a project that has created a Topic Map search tool for a mathematics educational database containing articles from the journal For the Learning of Mathematics.The resulting website enables users to retrieve research articles based on a variety of topics such as mathematics classification,research methods,educational objectives,in addition to traditional bibliographic information.展开更多
The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for ind...The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.展开更多
<div style="text-align:justify;"> Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient to...<div style="text-align:justify;"> Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time. </div>展开更多
The volume of information being created, generated and stored is huge. Without adequate knowledge of Information Retrieval (IR) methods, the retrieval process for information would be cumbersome and frustrating. Studi...The volume of information being created, generated and stored is huge. Without adequate knowledge of Information Retrieval (IR) methods, the retrieval process for information would be cumbersome and frustrating. Studies have further revealed that IR methods are essential in information centres (for example, Digital Library environment) for storage and retrieval of information. Therefore, with more than one billion people accessing the Internet, and millions of queries being issued on a daily basis, modern Web search engines are facing a problem of daunting scale. The main problem associated with the existing search engines is how to avoid irrelevant information retrieval and to retrieve the relevant ones. In this study, the existing system of library retrieval was studied. Problems associated with them were analyzed in order to address this problem. The concept of existing information retrieval models was studied, and the knowledge gained was used to design a digital library information retrieval system. It was successfully implemented using a real life data. The need for a continuous evaluation of the IR methods for effective and efficient full text retrieval system was recommended.展开更多
OOV term translation plays an important role in natural language processing. Although many researchers in the past have endeavored to solve the OOV term translation problems, but none existing methods offer definition...OOV term translation plays an important role in natural language processing. Although many researchers in the past have endeavored to solve the OOV term translation problems, but none existing methods offer definition or context information of OOV terms. Furthermore, non-existing methods focus on cross-language definition retrieval for OOV terms. Never the less, it has always been so difficult to evaluate the correctness of an OOV term translation without domain specific knowledge and correct references. Our English definition ranking method differentiate the types of OOV terms, and applies different methods for translation extraction. Our English definition ranking method also extracts multilingual context information and monolingual definitions of OOV terms. In addition, we propose a novel cross-language definition retrieval system for OOV terms. Never the less, we propose an auto re-evaluation method to evaluate the correctness of OOV translations and definitions. Our methods achieve high performances against existing methods.展开更多
In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts...In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...展开更多
Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered o...Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered orientation, training a CBIR system to detect and correct the angle is complex. While it is possible to construct rotation-invariant features by hand, retrieval accuracy will be low because hand engineering only creates low-level features, while deep learning methods build high-level and low-level features simultaneously. This paper presents a novel approach that combines a deep learning orientation angle detection model with the CBIR feature extraction model to correct the rotation angle of any image. This offers a unique construction of a rotation-invariant CBIR system that handles the CNN features that are not rotation invariant. This research also proposes a further study on how a rotation-invariant deep CBIR can recover images from the dataset in real-time. The final results of this system show significant improvement as compared to a default CNN feature extraction model without the OAD.展开更多
Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is n...Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is not an appropriate, because it gives ambiguous information for the user query, and the user cannot get relevant information within the stipulated time. To overcome these issues, we propose a new methodology for information retrieval EPCRR by providing the top most exact information to the user, by using the collaborative clustered automated filter which makes use of the collaborative data set and filter works on the prediction by providing the highest ranking for the exact data retrieved. The retrieval works on the basis of recommendation of data which consists of relevant data set with highest priority from the cluster of data which is on high usage. In this work, we make use of the automated wrapper which works similar to the meta crawler functionality and it obtains the content in the semantic usage data format. Obtained information from the user to the agent will be ranked based on the Enabled Pile clustered data with respect to the metadata information from the agent and end-user. The information is given to the end-user with the top most ranking data within the stipulated time and the remaining top information will be moved to the data repository for future use. The data collected will remain stable based on the user preference and works on the intelligence system approach in which the user can choose any information under any instances and can be provided with suitable high range of exact content. In this approach, we find that the proposed algorithm has produced better results than existing work and it costs less online computation time.展开更多
The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models...The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.展开更多
In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the dr...In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.展开更多
Daily newspapers publish a tremendous amount of information disseminated through the Internet.Freely available and easily accessible large online repositories are not indexed and are in an un-processable format.The ma...Daily newspapers publish a tremendous amount of information disseminated through the Internet.Freely available and easily accessible large online repositories are not indexed and are in an un-processable format.The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed.There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora,especially for South Asian languages.The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language.Lack of indexing,primarily semantic indexing of the daily news items,makes news items impracticable for any querying.Moreover,the most straightforward search facility does not support these unindexed news resources.Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages,i.e.,Urdu.The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler.The corpus is then semantically linked and annotated with daily news items.Two techniques are proposed for image annotation,free annotation and fixed cross examination annotation.The second technique got higher accuracy.Build news ontology in protégéusing OntologyWeb Language(OWL)language and indexed the annotations under it.The application is also built and linked with protégéso that the readers and journalists have an interface to query the news items directly.Similarly,news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.展开更多
At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct...At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure information.The adjacency matrix constructed by the dependency tree can convey syntactic information.Dependency trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic description.At the same time,a large amount of irrelevant information will cause redundancy.This paper presents a novel end-to-end entity and relation joint extraction based on the multihead attention graph convolutional network model(MAGCN),which does not rely on external tools.MAGCN generates an adjacency matrix through a multi-head attention mechanism to form an attention graph convolutional network model,uses head selection to identify multiple relations,and effectively improve the prediction result of overlapping relations.The authors extensively experiment and prove the method's effectiveness on three public datasets:NYT,WebNLG,and CoNLL04.The results show that the authors’method outperforms the state-of-the-art research results for the task of entities and relation extraction.展开更多
Powerize.com is well known for providing free business and financial information on the Web.The unique information integrated service of Powerize.com,its approaches to free information and fee based information as wel...Powerize.com is well known for providing free business and financial information on the Web.The unique information integrated service of Powerize.com,its approaches to free information and fee based information as well as its searching modes are discussed.The author suggests that information experts pay more attention to recommending such kinds of services to Internet users in China in order to let more and more Internet users obtain free information via such kinds of services.展开更多
基金supported by the National Key R&D Program of China(2020YFB0905900).
文摘Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy.Therefore,improvement of the ability of data-driven operation management,intelligent analysis,and mining is urgently required.To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation,maintenance experience,and knowledge by rule and line,a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology.Based on the processing flow of the operating data of the power distribution system,a technical framework of neural information retrieval is established.Combined with the natural graph characteristics of the power distribution system,a unified graph data structure and a data fusion method of data access,data complement,and multi-source data are constructed.Further,a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed.The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set.The model is verified on the operating section of the power distribution system of a provincial grid area.The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.
基金This work is financially supported by the Ministry of Earth Science(MoES),Government of India,(Grant.No.MoES/36/OOIS/Extra/45/2015),URL:https://www.moes.gov.in。
文摘The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.
文摘Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers.Familiarizing ontology as information retrieval(IR)aids in augmenting the searching effects of user-required relevant information.The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet,mapping the connection between keywords and information,and categorizing the retrieval outcomes.The prevailing procedures for IR consume considerable time,and they could not recover information proficiently.In this study,through applying a modified neuro-fuzzy algorithm(MNFA),the IR time is mitigated,and the retrieval accuracy is enhanced for trouncing the above-stated downsides.The proposed method encompasses three phases:i)development of a crop ontology,ii)implementation of the IR system,and iii)processing of user query.In the initial phase,a crop ontology is developed and evaluated by gathering crop information.In the next phase,a hash tree is constructed using closed frequent patterns(CFPs),and MNFA is used to train the database.In the last phase,for a specified user query,CFP is calculated,and similarity assessment results are retrieved using the database.The performance of the proposed system is measured and compared with that of existing techniques.Experimental results demonstrate that the proposed MNFA has an accuracy of 92.77% for simple queries and 91.45% for complex queries.
基金This work was supported in part by the National Natural Science Foundation of China,Grant No.72073041Open Foundation for the University Innovation Platform in the Hunan Province,Grant No.18K103.2011+2 种基金Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property.Hunan Provincial Key Laboratory of Finance&Economics Big Data Science and Technology2020 Hunan Provincial Higher Education Teaching Reform Research Project under Grant HNJG-2020-1130,HNJG-2020-11242020 General Project of Hunan Social Science Fund under Grant 20B16.
文摘With the development and progress of today’s network information technology,a variety of large-scale network databases have emerged with the situation,such as Baidu Library and Weipu Database,the number of documents in the inventory has reached nearly one million.So how do you quickly and effectively retrieve the information you want in such a huge database?This requires finding efficient algorithms to reduce the computational complexity of the computer during Information Retrieval,improve retrieval efficiency,and adapt to the rapid expansion of document data.The Quicksort Algorithm gives different weights to each position of the document,and multiplies the weight of each position with the number of matches of that position,and then adds all the multiplied sums to set a feature value for Quicksort,which can achieve the full accuracy of Information Retrieval.Therefore,the purpose of this paper is to use the quick sort algorithm to increase the speed of Information Retrieval,and to use the position weighting algorithm to improve the matching quality of Information Retrieval,so as to achieve the overall effect of improving the efficiency of Information Retrieval.
文摘Objective: Information visualization is the study of interactive depictions of abstract and data to strengthen the human cognition. Designing an appropriate information visualization system may be very useful technique for scholars, who intent to get scientific information from digital libraries. The objective of current study was to map and visualize the key-information of dissertations in academic libraries. To achieve the aim, an information retrieval system was designed to present the interactive graphic view of dissertations’ subjects in academic. Methods: An information retrieval system was designed by information visualization toolkit that presents the related subjects of dissertations in academic libraries. In addition, the satisfaction-levels of library-users were analyzed by administrating a standard questionnaire (QUIS Questionnaire). Results: The study indicated that the designed IR system helped to provide a user-friendly environment through displaying subjective relations of dissertations, overwhelming variety of colors in displaying information. Fast and easy access to the cover-to-cover information of dissertations and user-interaction facilities are the advantages of designed IR. Analysis of data furthermore indicated that the users’ satisfaction from the system was from medium to high grade. Conclusion: Designing the IR-system revealed an excessive influence on users’ satisfaction;therefore, proposing such systems for employing in academic libraries is very suitable and its implementation is necessary.
文摘During a two day strategic workshop in February 2018,22 information retrieval researchers met to discuss the future challenges and opportunities within the field.The outcome is a list of potential research directions,project ideas,and challenges.This report describes the major conclusions we have obtained during the workshop.A key result is that we need to open our mind to embrace a broader IR field by rethink the definition of information,retrieval,user,system,and evaluation of IR.By providing detailed discussions on these topics,this report is expected to inspire our IR researchers in both academia and industry,and help the future growth of the IR research community.
文摘Neural attention-based encoders,which effectively attend sentence tokens to their associated context without being restricted by long-term distance or dependency,have demonstrated outstanding performance in embedding sentences into meaningful representations(embeddings).The Universal Sentence Encoder(USE)is one of the most well-recognized deep neural network(DNN)based solutions,which is facilitated with an attention-driven transformer architecture and has been pre-trained on a large number of sentences from the Internet.Besides the fact that USE has been widely used in many downstream applications,including information retrieval(IR),interpreting its complicated internal working mechanism remains challenging.In this work,we present a visual analytics solution towards addressing this challenge.Specifically,focused on semantics and syntactics(concepts and relations)that are critical to domain clinical IR,we designed and developed a visual analytics system,i.e.,USEVis.The system investigates the power of USE in effectively extracting sentences’semantics and syntactics through exploring and interpreting how linguistic properties are captured by attentions.Furthermore,by thoroughly examining and comparing the inherent patterns of these attentions,we are able to exploit attentions to retrieve sentences/documents that have similar semantics or are closely related to a given clinical problem in IR.By collaborating with domain experts,we demonstrate use cases with inspiring findings to validate the contribution of our work and the effectiveness of our system.
文摘This paper describes a project that has created a Topic Map search tool for a mathematics educational database containing articles from the journal For the Learning of Mathematics.The resulting website enables users to retrieve research articles based on a variety of topics such as mathematics classification,research methods,educational objectives,in addition to traditional bibliographic information.
文摘The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.
文摘<div style="text-align:justify;"> Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time. </div>
文摘The volume of information being created, generated and stored is huge. Without adequate knowledge of Information Retrieval (IR) methods, the retrieval process for information would be cumbersome and frustrating. Studies have further revealed that IR methods are essential in information centres (for example, Digital Library environment) for storage and retrieval of information. Therefore, with more than one billion people accessing the Internet, and millions of queries being issued on a daily basis, modern Web search engines are facing a problem of daunting scale. The main problem associated with the existing search engines is how to avoid irrelevant information retrieval and to retrieve the relevant ones. In this study, the existing system of library retrieval was studied. Problems associated with them were analyzed in order to address this problem. The concept of existing information retrieval models was studied, and the knowledge gained was used to design a digital library information retrieval system. It was successfully implemented using a real life data. The need for a continuous evaluation of the IR methods for effective and efficient full text retrieval system was recommended.
文摘OOV term translation plays an important role in natural language processing. Although many researchers in the past have endeavored to solve the OOV term translation problems, but none existing methods offer definition or context information of OOV terms. Furthermore, non-existing methods focus on cross-language definition retrieval for OOV terms. Never the less, it has always been so difficult to evaluate the correctness of an OOV term translation without domain specific knowledge and correct references. Our English definition ranking method differentiate the types of OOV terms, and applies different methods for translation extraction. Our English definition ranking method also extracts multilingual context information and monolingual definitions of OOV terms. In addition, we propose a novel cross-language definition retrieval system for OOV terms. Never the less, we propose an auto re-evaluation method to evaluate the correctness of OOV translations and definitions. Our methods achieve high performances against existing methods.
文摘In medical research and clinical diagnosis, automated or computer-assisted classification and retrieval methods are highly desirable to offset the high cost of manual classification and manipulation by medical experts. To facilitate the decision-making in the health-care and the related areas, in this paper, a two-step content-based medical image retrieval algorithm is proposed. Firstly, in the preprocessing step, the image segmentation is performed to distinguish image objects, and on the basis of the ...
文摘Over recent years, Convolutional Neural Networks (CNN) has improved performance on practically every image-based task, including Content-Based Image Retrieval (CBIR). Nevertheless, since features of CNN have altered orientation, training a CBIR system to detect and correct the angle is complex. While it is possible to construct rotation-invariant features by hand, retrieval accuracy will be low because hand engineering only creates low-level features, while deep learning methods build high-level and low-level features simultaneously. This paper presents a novel approach that combines a deep learning orientation angle detection model with the CBIR feature extraction model to correct the rotation angle of any image. This offers a unique construction of a rotation-invariant CBIR system that handles the CNN features that are not rotation invariant. This research also proposes a further study on how a rotation-invariant deep CBIR can recover images from the dataset in real-time. The final results of this system show significant improvement as compared to a default CNN feature extraction model without the OAD.
文摘Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is not an appropriate, because it gives ambiguous information for the user query, and the user cannot get relevant information within the stipulated time. To overcome these issues, we propose a new methodology for information retrieval EPCRR by providing the top most exact information to the user, by using the collaborative clustered automated filter which makes use of the collaborative data set and filter works on the prediction by providing the highest ranking for the exact data retrieved. The retrieval works on the basis of recommendation of data which consists of relevant data set with highest priority from the cluster of data which is on high usage. In this work, we make use of the automated wrapper which works similar to the meta crawler functionality and it obtains the content in the semantic usage data format. Obtained information from the user to the agent will be ranked based on the Enabled Pile clustered data with respect to the metadata information from the agent and end-user. The information is given to the end-user with the top most ranking data within the stipulated time and the remaining top information will be moved to the data repository for future use. The data collected will remain stable based on the user preference and works on the intelligence system approach in which the user can choose any information under any instances and can be provided with suitable high range of exact content. In this approach, we find that the proposed algorithm has produced better results than existing work and it costs less online computation time.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the|Deanship of Scientific Research at Umm Al-Qura University|for supporting this work by Grant Code:(22UQU4310373DSR33).
文摘The recent developments in Multimedia Internet of Things(MIoT)devices,empowered with Natural Language Processing(NLP)model,seem to be a promising future of smart devices.It plays an important role in industrial models such as speech understanding,emotion detection,home automation,and so on.If an image needs to be captioned,then the objects in that image,its actions and connections,and any silent feature that remains under-projected or missing from the images should be identified.The aim of the image captioning process is to generate a caption for image.In next step,the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct.In this scenario,computer vision model is used to identify the objects and NLP approaches are followed to describe the image.The current study develops aNatural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System(NLPODL-IICS).The aim of the presented NLPODL-IICS model is to produce a proper description for input image.To attain this,the proposed NLPODL-IICS follows two stages such as encoding and decoding processes.Initially,at the encoding side,the proposed NLPODL-IICS model makes use of Hunger Games Search(HGS)with Neural Search Architecture Network(NASNet)model.This model represents the input data appropriately by inserting it into a predefined length vector.Besides,during decoding phase,Chimp Optimization Algorithm(COA)with deeper Long Short Term Memory(LSTM)approach is followed to concatenate the description sentences 4436 CMC,2023,vol.74,no.2 produced by the method.The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively.The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets.Awidespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.
基金The research was funded by the School of Information Technology and Engineering,Vellore Institute of Technology,Vellore 632014,Tamil Nadu,India.
文摘In crowded cities,searching for the availability of parking lots is a herculean task as it results in the wastage of drivers’time,increases air pollution,and traffic congestion.Smart parking systems facilitate the drivers to determine the information about the parking lot in real time and book them depending on the requirement.But the existing smart parking systems necessitate the drivers to reveal their sensitive information that includes their mobile number,personal identity,and desired destination.This disclosure of sensitive information makes the existing centralized smart parking systems more vulnerable to service providers’security breaches,single points of failure,and bottlenecks.In this paper,an Improved Asymmetric Consortium Blockchain and Homomorphically Computing Univariate Polynomial-based private information retrieval(IACB-HCUPPIR)scheme is proposed to ensure parking lots’availability with transparency security in a privacy-preserving smart parking system.In specific,an improved Asymmetric Consortium Blockchain is used for achieving secure transactions between different parties interacting in the smart parking environment.It further adopted the method of Homomorphically Computing Univariate Polynomial-based private information retrieval(HCUPPIR)scheme for preserving the location privacy of drivers.The results of IACB-HCUPPIR confirmed better results in terms of minimized computation and communication overload with throughput,latency,and response time with maximized drivers’privacy preservation.Moreover,the proposed fully homomorphic algorithm(FHE)was compared against partial-homomorphic encryption(PHE)and technique without encryption and found that the proposed model has quick communication in allocating the parking slots starting with 24.3 s,whereas PHE starts allocating from 24.7 s and the technique without encryption starts at 27.4 s.Thus,we ensure the proposed model performs well in allocating parking slots with less time and high security with privacy preservation.
基金King Saud University through Researchers Supporting Project number(RSP-2021/387),King Saud University,Riyadh,Saudi Arabia.
文摘Daily newspapers publish a tremendous amount of information disseminated through the Internet.Freely available and easily accessible large online repositories are not indexed and are in an un-processable format.The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed.There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora,especially for South Asian languages.The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language.Lack of indexing,primarily semantic indexing of the daily news items,makes news items impracticable for any querying.Moreover,the most straightforward search facility does not support these unindexed news resources.Our study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages,i.e.,Urdu.The present work proposed a method to build a benchmark corpus of news in image form by introducing a web crawler.The corpus is then semantically linked and annotated with daily news items.Two techniques are proposed for image annotation,free annotation and fixed cross examination annotation.The second technique got higher accuracy.Build news ontology in protégéusing OntologyWeb Language(OWL)language and indexed the annotations under it.The application is also built and linked with protégéso that the readers and journalists have an interface to query the news items directly.Similarly,news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.
基金State Key Program of National Natural Science of China,Grant/Award Number:61533018National Natural Science Foundation of China,Grant/Award Number:61402220+2 种基金Philosophy and Social Science Foundation of Hunan Province,Grant/Award Number:16YBA323Natural Science Foundation of Hunan Province,Grant/Award Number:2020JJ4525Scientific Research Fund of Hunan Provincial Education Department,Grant/Award Numbers:18B279,19A439。
文摘At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure information.The adjacency matrix constructed by the dependency tree can convey syntactic information.Dependency trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic description.At the same time,a large amount of irrelevant information will cause redundancy.This paper presents a novel end-to-end entity and relation joint extraction based on the multihead attention graph convolutional network model(MAGCN),which does not rely on external tools.MAGCN generates an adjacency matrix through a multi-head attention mechanism to form an attention graph convolutional network model,uses head selection to identify multiple relations,and effectively improve the prediction result of overlapping relations.The authors extensively experiment and prove the method's effectiveness on three public datasets:NYT,WebNLG,and CoNLL04.The results show that the authors’method outperforms the state-of-the-art research results for the task of entities and relation extraction.
文摘Powerize.com is well known for providing free business and financial information on the Web.The unique information integrated service of Powerize.com,its approaches to free information and fee based information as well as its searching modes are discussed.The author suggests that information experts pay more attention to recommending such kinds of services to Internet users in China in order to let more and more Internet users obtain free information via such kinds of services.
