PeerJ Computer Science最新文献_第9页

Automated programming approaches to enhance computer-aided translation accuracy.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-12 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2396

Tao Zhao, Mazni Binti Alias

{"title":"Automated programming approaches to enhance computer-aided translation accuracy.","authors":"Tao Zhao, Mazni Binti Alias","doi":"10.7717/peerj-cs.2396","DOIUrl":"10.7717/peerj-cs.2396","url":null,"abstract":"With the continued development of information technology and increased global cultural exchanges, translation has gained significant attention. Traditional manual translation relies heavily on dictionaries or personal experience, translating word by word. While this method ensures high translation quality, it is often too slow to meet the demands of today's fast-paced environment. Computer-assisted translation (CAT) addresses the issue of slow translation speed; however, the quality of CAT translations still requires rigorous evaluation. This study aims to answer the following questions: How do CAT systems that use automated programming fare compared to more conventional methods of human translation when translating English vocabulary? (2) How can CAT systems be improved to handle difficult English words, specialised terminology, and semantic subtleties? The working premise is that CAT systems that use automated programming techniques will outperform traditional methods in terms of translation accuracy. English vocabulary plays a crucial role in translation, as words can have different meanings depending on the context. CAT systems improve their translation accuracy by utilising specific automated programs and building a translation corpus through translation memory technology. This study compares the accuracy of English vocabulary translations produced by CAT based on automatic programming with those produced by traditional manual translation. Experimental results demonstrate that CAT based on automatic programming is 8% more accurate than traditional manual translation when dealing with complex English vocabulary sentences, professional jargon, English acronyms, and semantic nuances. Consequently, compared to conventional human translation, CAT can enhance the accuracy of English vocabulary translation, making it a valuable tool in the translation industry.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2396"},"PeriodicalIF":3.5,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging machine learning for the detection of structured interference in Global Navigation Satellite Systems.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-11 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2399

Imtiaz Nabi, Salma Zainab Farooq, Sunnyaha Saeed, Syed Ali Irtaza, Khurram Shehzad, Mohammad Arif, Inayat Khan, Shafiq Ahmad

{"title":"Leveraging machine learning for the detection of structured interference in Global Navigation Satellite Systems.","authors":"Imtiaz Nabi, Salma Zainab Farooq, Sunnyaha Saeed, Syed Ali Irtaza, Khurram Shehzad, Mohammad Arif, Inayat Khan, Shafiq Ahmad","doi":"10.7717/peerj-cs.2399","DOIUrl":"10.7717/peerj-cs.2399","url":null,"abstract":"Radio frequency interference disrupts services offered by Global Navigation Satellite Systems (GNSS). Spoofing is the transmission of structured interference signals intended to deceive GNSS location and timing services. The identification of spoofing is vital, especially for safety-of-life aviation services, since the receiver is unaware of counterfeit signals. Although numerous spoofing detection and mitigation techniques have been developed, spoofing attacks are becoming more sophisticated, limiting most of these methods. This study explores the application of machine learning techniques for discerning authentic signals from counterfeit ones. The investigation particularly focuses on the secure code estimation and replay (SCER) spoofing attack, one of the most challenging type of spoofing attacks, ds8 scenario of the Texas Spoofing Test Battery (TEXBAT) dataset. The proposed framework uses tracking data from delay lock loop correlators as intrinsic features to train four distinct machine learning (ML) models: logistic regression, support vector machines (SVM) classifier, K-nearest neighbors (KNN), and decision tree. The models are trained employing a random six-fold cross-validation methodology. It can be observed that both logistic regression and SVM can detect spoofing with a mean F1-score of 94%. However, logistic regression provides 165dB gain in terms of time efficiency as compared to SVM and 3 better than decision tree-based classifier. These performance metrics as well as receiver operating characteristic curve analysis make logistic regression the desirable approach for identifying SCER structured interference.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2399"},"PeriodicalIF":3.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised learning analysis on the proteomes of Zika virus.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-11 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2443

Edgar E Lara-Ramírez, Gildardo Rivera, Amanda Alejandra Oliva-Hernández, Virgilio Bocanegra-Garcia, Jesús Adrián López, Xianwu Guo

{"title":"Unsupervised learning analysis on the proteomes of Zika virus.","authors":"Edgar E Lara-Ramírez, Gildardo Rivera, Amanda Alejandra Oliva-Hernández, Virgilio Bocanegra-Garcia, Jesús Adrián López, Xianwu Guo","doi":"10.7717/peerj-cs.2443","DOIUrl":"10.7717/peerj-cs.2443","url":null,"abstract":"Background: The Zika virus (ZIKV), which is transmitted by mosquito vectors to nonhuman primates and humans, causes devastating outbreaks in the poorest tropical regions of the world. Molecular epidemiology, supported by clustering phylogenetic gold standard studies using sequence data, has provided valuable information for tracking and controlling the spread of ZIKV. Unsupervised learning (UL), a form of machine learning algorithm, can be applied on the datasets without the need of known information for training.Methods: In this work, unsupervised Random Forest (URF), followed by the application of dimensional reduction algorithms such as principal component analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders were used to uncover hidden patterns from polymorphic amino acid sites extracted on the proteome ZIKV multi-alignments, without the need of an underlying evolutionary model.Results: The four UL algorithms revealed specific host and geographical clustering patterns for ZIKV. Among the four dimensionality reduction (DR) algorithms, the performance was better for UMAP. The four algorithms allowed the identification of imported viruses for specific geographical clusters. The UL dimension coordinates showed a significant correlation with phylogenetic tree branch lengths and significant phylogenetic dependence in Abouheif's Cmean and Pagel's Lambda tests (p value < 0.01) that showed comparable performance with the phylogenetic method. This analytical strategy was generalizable to an external large dengue type 2 dataset.Conclusion: These UL algorithms could be practical evolutionary analytical techniques to track the dispersal of viral pathogens.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2443"},"PeriodicalIF":3.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative analysis of variants of machine learning and time series models in predicting women's participation in the labor force.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-11 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2430

Rasha Elstohy, Nevein Aneis, Eman Mounir Ali

{"title":"A comparative analysis of variants of machine learning and time series models in predicting women's participation in the labor force.","authors":"Rasha Elstohy, Nevein Aneis, Eman Mounir Ali","doi":"10.7717/peerj-cs.2430","DOIUrl":"10.7717/peerj-cs.2430","url":null,"abstract":"Labor force participation of Egyptian women has been a chronic economic problem in Egypt. Despite the improvement in the human capital front, whether on the education or health indicators, female labor force participation remains persistently low. This study proposes a hybrid machine-learning model that integrates principal component analysis (PCA) for feature extraction with various machine learning and time-series models to predict women's employment in times of crisis. Various machine learning (ML) algorithms, such as support vector machine (SVM), neural network, K-nearest neighbor (KNN), linear regression, random forest, and AdaBoost, in addition to popular time series algorithms, including autoregressive integrated moving average (ARIMA) and vector autoregressive (VAR) models, have been applied to an actual dataset from the public sector. The manpower dataset considered gender from different regions, ages, and educational levels. The dataset was then trained, tested, and evaluated. For performance validation, forecasting accuracy metrics were constructed using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), R-squared (R2), and cross-validated root mean squared error (CVRMSE). Another Dickey-Fuller test was performed to evaluate and compare the accuracy of the applied models, and the results showed that AdaBoost outperforms the other methods by an accuracy of 100%. Compared to alternative works, our findings demonstrate a comprehensive comparative analysis for predicting women's participation in different regions during an economic crisis.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2430"},"PeriodicalIF":3.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive learning algorithm based price prediction model for auction lots-deep clustering based interval quoting.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-07 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2412

Da Ke, Xianhua Fan, Muhammad Asif

{"title":"Adaptive learning algorithm based price prediction model for auction lots-deep clustering based interval quoting.","authors":"Da Ke, Xianhua Fan, Muhammad Asif","doi":"10.7717/peerj-cs.2412","DOIUrl":"10.7717/peerj-cs.2412","url":null,"abstract":"This article addresses the problem of interval pricing for auction items by constructing an auction item price prediction model based on an adaptive learning algorithm. Firstly, considering the confusing class characteristics of auction item prices, a dynamic inter-class distance adaptive learning model is developed to identify confusing classes by calculating the differences in prediction values across multiple classifiers for target domain samples. The difference in the predicted values of the target domain samples on multiple classifiers is used to calculate the classification distance, distinguish the confusing classes, and make the similar samples in the target domain more clustered. Secondly, a deep clustering algorithm is constructed, which integrates the temporal characteristics and numerical differences of auction item prices, using DTW-K-medoids based dynamic time warping (DTW) and fuzzy C-means (FCM) algorithms for fine clustering. Finally, the KF-LSTM auction item interval price prediction model is constructed using long short-term memory (LSTM) and dual clustering. Experimental results show that the proposed KF-LSTM model significantly improves the prediction accuracy of auction item prices during fluctuation periods, with an average accuracy rate of 90.23% and an average MAPE of only 5.41%. Additionally, under confidence levels of 80%, 85%, and 90%, the KF-LSTM model achieves an interval coverage rate of over 85% for actual auction item prices, significantly enhancing the accuracy of auction item price predictions. This experiment demonstrates the stability and accuracy of the proposed model when applied to different sets of auction items, providing a valuable reference for research in the auction item price prediction field.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2412"},"PeriodicalIF":3.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623274/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genome-wide association studies of ischemic stroke based on interpretable machine learning.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-06 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2454

Stefan Nikolić, Dmitry I Ignatov, Gennady V Khvorykh, Svetlana A Limborska, Andrey V Khrunin

{"title":"Genome-wide association studies of ischemic stroke based on interpretable machine learning.","authors":"Stefan Nikolić, Dmitry I Ignatov, Gennady V Khvorykh, Svetlana A Limborska, Andrey V Khrunin","doi":"10.7717/peerj-cs.2454","DOIUrl":"10.7717/peerj-cs.2454","url":null,"abstract":"Despite the identification of several dozen genetic loci associated with ischemic stroke (IS), the genetic bases of this disease remain largely unexplored. In this research we present the results of genome-wide association studies (GWAS) based on classical statistical testing and machine learning algorithms (logistic regression, gradient boosting on decision trees, and tabular deep learning model TabNet). To build a consensus on the results obtained by different techniques, the Pareto-Optimal solution was proposed and applied. These methods were applied to real genotypic data of sick and healthy individuals of European ancestry obtained from the Database of Genotypes and Phenotypes (5,581 individuals, 883,749 single nucleotide polymorphisms). Finally, 131 genes were identified as candidates for association with the onset of IS. UBQLN1, TRPS1, and MUSK were previously described as associated with the course of IS in model animals. ACOT11 taking part in metabolism of fatty acids was shown for the first time to be associated with IS. The identified genes were compared with genes from the Illuminating Druggable Genome project. The product of GPR26 representing the G-coupled protein receptor can be considered as a therapeutic target for stroke prevention. The approaches presented in this research can be used to reprocess GWAS datasets from other diseases.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2454"},"PeriodicalIF":3.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-06 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2448

Mohammed A H Ali, Khaja Moiduddin, Yusoff Nukman, Bushroa Abd Razak, Mohamed K Aboudaif, Muthuramalingam Thangaraj

{"title":"An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating.","authors":"Mohammed A H Ali, Khaja Moiduddin, Yusoff Nukman, Bushroa Abd Razak, Mohamed K Aboudaif, Muthuramalingam Thangaraj","doi":"10.7717/peerj-cs.2448","DOIUrl":"https://doi.org/10.7717/peerj-cs.2448","url":null,"abstract":"This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests' detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2448"},"PeriodicalIF":3.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Positive Unlabeled Learning Selected Not At Random (PULSNAR): class proportion estimation without the selected completely at random assumption.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-05 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2451

Praveen Kumar, Christophe G Lambert

{"title":"Positive Unlabeled Learning Selected Not At Random (PULSNAR): class proportion estimation without the selected completely at random assumption.","authors":"Praveen Kumar, Christophe G Lambert","doi":"10.7717/peerj-cs.2451","DOIUrl":"10.7717/peerj-cs.2451","url":null,"abstract":"Positive and unlabeled (PU) learning is a type of semi-supervised binary classification where the machine learning algorithm differentiates between a set of positive instances (labeled) and a set of both positive and negative instances (unlabeled). PU learning has broad applications in settings where confirmed negatives are unavailable or difficult to obtain, and there is value in discovering positives among the unlabeled (e.g., viable drugs among untested compounds). Most PU learning algorithms make the selected completely at random (SCAR) assumption, namely that positives are selected independently of their features. However, in many real-world applications, such as healthcare, positives are not SCAR (e.g., severe cases are more likely to be diagnosed), leading to a poor estimate of the proportion, α, of positives among unlabeled examples and poor model calibration, resulting in an uncertain decision threshold for selecting positives. PU learning algorithms vary; some estimate only the proportion, α, of positives in the unlabeled set, while others calculate the probability that each specific unlabeled instance is positive, and some can do both. We propose two PU learning algorithms to estimate α, calculate calibrated probabilities for PU instances, and improve classification metrics: i) PULSCAR (positive unlabeled learning selected completely at random), and ii) PULSNAR (positive unlabeled learning selected not at random). PULSNAR employs a divide-and-conquer approach to cluster SNAR positives into subtypes and estimates α for each subtype by applying PULSCAR to positives from each cluster and all unlabeled. In our experiments, PULSNAR outperformed state-of-the-art approaches on both synthetic and real-world benchmark datasets.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2451"},"PeriodicalIF":3.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-05 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2401

Rhodes Massenon, Ishaya Gambo, Roseline Oluwaseun Ogundokun, Ezekiel Adebayo Ogundepo, Sweta Srivastava, Saurabh Agarwal, Wooguil Pak

{"title":"Mobile app review analysis for crowdsourcing of software requirements: a mapping study of automated and semi-automated tools.","authors":"Rhodes Massenon, Ishaya Gambo, Roseline Oluwaseun Ogundokun, Ezekiel Adebayo Ogundepo, Sweta Srivastava, Saurabh Agarwal, Wooguil Pak","doi":"10.7717/peerj-cs.2401","DOIUrl":"10.7717/peerj-cs.2401","url":null,"abstract":"Mobile app reviews are valuable for gaining user feedback on features, usability, and areas for improvement. Analyzing these reviews manually is difficult due to volume and structure, leading to the need for automated techniques. This mapping study categorizes existing approaches for automated and semi-automated tools by analyzing 180 primary studies. Techniques include topic modeling, collocation finding, association rule-based, aspect-based sentiment analysis, frequency-based, word vector-based, and hybrid approaches. The study compares various tools for analyzing mobile app reviews based on performance, scalability, and user-friendliness. Tools like KEFE, MERIT, DIVER, SAFER, SIRA, T-FEX, RE-BERT, and AOBTM outperformed baseline tools like IDEA and SAFE in identifying emerging issues and extracting relevant information. The study also discusses limitations such as manual intervention, linguistic complexities, scalability issues, and interpretability challenges in incorporating user feedback. Overall, this mapping study outlines the current state of feature extraction from app reviews, suggesting future research and innovation opportunities for extracting software requirements from mobile app reviews, thereby improving mobile app development.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2401"},"PeriodicalIF":3.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Developing a digital management system for museum collections using RFID and enhanced GIS technology.

IF 3.5 4区计算机科学

PeerJ Computer Science Pub Date : 2024-11-04 eCollection Date: 2024-01-01 DOI: 10.7717/peerj-cs.2462

Yun Wang, Ying Zhang, LingYu Zhang

{"title":"Developing a digital management system for museum collections using RFID and enhanced GIS technology.","authors":"Yun Wang, Ying Zhang, LingYu Zhang","doi":"10.7717/peerj-cs.2462","DOIUrl":"10.7717/peerj-cs.2462","url":null,"abstract":"In recent years, the integration of Radio Frequency Identification (RFID) technology with deep learning has revolutionized the Internet of Things (IoT), leading to significant advancements in object identification, management, and control. Museums, which rely heavily on the meticulous management of collections, require precise and efficient systems to monitor and oversee their valuable assets. Traditional methods for tracking and managing museum collections often fall short in providing real-time updates and ensuring optimal environmental conditions for preservation. These shortcomings place a considerable burden on museum staff, who must manually track, inspect, and maintain extensive collections. This study addresses these challenges by proposing an advanced electronic management system that leverages the synergy between RFID technology and Geographical Information Systems (GIS). By integrating an enhanced LANDMARC algorithm into our geoinformation framework, the system visually represents the real-time location of museum collections on custom electronic maps, significantly improving the accuracy and timeliness of environmental monitoring. Additionally, RFID technology is utilized to continuously identify the real-time location of museum staff, facilitating the evaluation of their inspection tasks. This dual approach not only enhances the operational efficiency of collection management but also supports the development of intelligent, automated systems for museums, advancing the application of RFID technology in item identification and location management.","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2462"},"PeriodicalIF":3.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623135/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0