EAI Endorsed Transactions on Scalable Information Systems最新文献

{"title":"An approach to financial information analysis by the Brazilian Federal Police","authors":"Renato Kettner Filho, D. D. J. D. Macedo","doi":"10.4108/eetsis.vi.3360","DOIUrl":"https://doi.org/10.4108/eetsis.vi.3360","url":null,"abstract":"INTRODUCTION: One of the tasks performed by the Federal Police is the verification and cross-referencing of data contained in Financial Intelligence Reports (FIRs) produced and forwarded by the Financial Activities Control Council (COAF) - an activity in which, among the police involved, the absence of a standard system of execution. \u0000OBJECTIVES: The present work aims to present a study on the form currently in use within the scope of the Federal Police Station regarding the analysis of FIRs from the COAF, then presenting a methodology suggestion, aiming to speed up the process. Regarding open sources, the objective is to carry out a survey of possible complementary repositories not yet used and to expose ways of implementing queries. \u0000METHODS: Pointing out the workflow currently used (displaying it in graphic form) as well as identifying the data sources consulted (open sources and closed sources) during the process, \u0000CONCLUSION: understanding how the FIR analysis system is currently carried out, identifying possible aspects for improvement, and suggesting a methodology to be used, indicating for this the use of files in a specific format (.CSV), exclusion of queries in similar repositories (System “A”) and, mainly, the automation of part of the procedure (with the use of the RIBOT prototype software).","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80171461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Breast Cancer with Ensemble Methods on Cloud","authors":"Au Pham, T. Tran, Phuc Tran, H. Huynh","doi":"10.4108/eetcasa.v8i2.2788","DOIUrl":"https://doi.org/10.4108/eetcasa.v8i2.2788","url":null,"abstract":"There are many dangerous diseases and high mortality rates for women (including breast cancer). If the disease is detected early, correctly diagnosed and treated at the right time, the likelihood of illness and death is reduced. Previous disease prediction models have mainly focused on methods for building individual models. However, these predictive models do not yet have high accuracy and high generalization performance. In this paper, we focus on combining these individual models together to create a combined model, which is more generalizable than the individual models. Three ensemble techniques used in the experiment are: Bagging; Boosting and Stacking (Stacking include three models: Gradient Boost, Random Forest, Logistic Regression) to deploy and apply to breast cancer prediction problem. The experimental results show the combined model with the ensemble methods based on the Breast Cancer Wisconsin dataset; this combined model has a higher predictive performance than the commonly used individual prediction models.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85959472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Happy Housework: Scenario-Based Experience Design for a Household Cleaning Robotic System","authors":"Yichen Lu, Zheng Liao","doi":"10.4108/eetsis.v10i3.2950","DOIUrl":"https://doi.org/10.4108/eetsis.v10i3.2950","url":null,"abstract":"INTRODUCTION: In the interwoven trend of the experience economy and advanced information technology, user experience becomes the substantial value of an interactive system. As one of the early innovations of a smart home, the current design of household cleaning robots is still driven by technology with a focus on pragmatic quality rather than the experiential value of a robotic system.OBJECTIVES: This paper aims to uplift the design vision of a cleaning robot from an automatic household appliance towards a meaningful robotic system engaging users in happy housework.METHODS: Theoretically, experience design and scenario-based design methods were combined into a specific design framework for domestic cleaning robotic systems. Based on the user study and technology trend analysis, we first set three experience goals (immersion, trust, and inspiration) to drive the design process, then chose 3D point cloud and AI recognition as backup technologies and afterwards extracted three main design scenarios (scanning and mapping, intelligent cleaning, and live control).RESULTS: The design features multi-view switching, a combination of animation rendering and real scene, fixed-point cleaning, map management, lens control and flexible remote, and shooting modes are proposed. Seventy-one participants evaluated the concept with online AttrakDiff questionnaires. The results indicate the targeted experience is fulfilled in the design concept.CONCLUSION: By integrating experience design and scenario-based design methods with technology trend analysis, designers can envision experiential scenarios of meaningful life and potentially expand the design opportunity space of interactive systems.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76587110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Transmission of Digital Grid Assisted by Intelligent Relaying","authors":"Shuangbai He, C. Yang, Yuda Li, Binyun Xie, Jiaqi Zhao","doi":"10.4108/eetsis.v10i3.2823","DOIUrl":"https://doi.org/10.4108/eetsis.v10i3.2823","url":null,"abstract":"In this paper, we study the relaying and cache aided digital grid data transmission, where the relaying may be equipped by caching or not, depending on specific applications. For both cases, we evaluate the impact of relaying and caching on the system performance of digital grid data transmission through theoretical derivation. To this end, an analytical expression on the outage probability is firstly derived for the data transmission. We then provide an asymptotic expression on the system outage probability. Finally, some simulation results are provided to verify the correctness of the derived analysis on the system performance, and show the impact of relaying and caching on the data transmission of digital grid system. In particular, the usage of caching at the relaying can help strengthen the data transmission performance of the considered system effectively. The results in this paper could provide some reference to the development of wireless transmission and scalable information systems.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74511146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Google Maps Data Analysis of Clothing Brands in South Punjab, Pakistan","authors":"Muḥammad Aḥmad, Kazim Jawad, Muhammad Bux Alvi, M. Alvi","doi":"10.4108/eetsis.v10i3.2677","DOIUrl":"https://doi.org/10.4108/eetsis.v10i3.2677","url":null,"abstract":"The Internet is a popular and first-hand source of data about products and services. Before buying a product, people try to gain quick insight by scanning through online reviews about a targeted product. However, searching for a product, collecting all the relevant information, and reaching a decision is a tedious task that needs to be automated. Such composed decision-assisting text data analysis systems are not conveniently available worldwide. Such systems are a dream for major cities of South Punjab, such as Bahawalpur, Multan, and Rahimyar khan. This scenario creates a gap that needs to be filled. In this work, the popularity of clothing brands in three cities of south Punjab has been assessed by analysing the brand's popularity using sentiment analysis by prioritizing brands based on organic feedback from their potential customers. This study uses a combination of quantitative and qualitative research to examine online reviews from Google Maps. The task is accomplished by applying machine learning techniques, Logistic Regression (LR), and Support Vector Machine (SVM), on Google Maps reviews data using the n-gram feature extraction approach. The SVM algorithm proved to be better than others with the uni-bi-trigram features extraction method, achieving an average of 80.93% accuracy.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72875436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Approach for Prediction of Gestational Diabetes based on Clinical Signs and Risk Factors","authors":"S. Reddy, Mahesh Gadiraju, N. Preethi, V.V.R.Maheswara Rao, Researc H Article","doi":"10.4108/eetsis.v10i3.2697","DOIUrl":"https://doi.org/10.4108/eetsis.v10i3.2697","url":null,"abstract":"Gestational diabetes mellitus occurs due to high glucose levels in the blood. Pregnant women are affected by this type of diabetes. A blood test is to be performed to identify diabetes. The Oral Glucose Tolerance Test (OGTT) is a blood test performed between the 24th and 28th week of pregnancy that is necessary to identify and overcome the side effects of GDM. The main objective of this work is to train a model by utilizing the training data, evaluate the trained model using the test data, and compare existing machine learning algorithms with a Gradient boosting machine (GBM) to achieve a better model for the effective prediction of gestational diabetes. In this work, the analysis was done with a few existing algorithms and the Extreme learning machine and Gradient boosting techniques. The k-fold cross-validation technique is applied with values of k as 3, 5, and 10 to obtain better performance. The existing algorithms implemented are the Naive Bayes classifier, Support Vector Machine, K-Nearest Neighbour, ID3, CART and J48. The proposed algorithms are Gradient boosting and ELM. These algorithms are implemented in R programming. The metrics like accuracy, kappa statistic, sensitivity/Recall, specificity, precision, f-measure and AUC are used to compare all the algorithms. GBM has obtained better performance than existing algorithms. Then finally, GBM is compared with the other proposed robust Machine Learning algorithm, namely the Extreme learning machine, and the GBM performed better. So, It is recommended to use a gradient-boosting algorithm to predict gestational diabetes effectively.\u0000 ","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72367793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure Data Processing Technology of Distribution Network OPGW Line with Edge Computing","authors":"Ying Zeng, Zhongmiao Kang, Zhan Shi","doi":"10.4108/eetsis.v10i3.2837","DOIUrl":"https://doi.org/10.4108/eetsis.v10i3.2837","url":null,"abstract":"Promoted by information technology and scalable information systems, the network design and communication method of optical fiber composite overhead ground wire (OPGW) have been in great progress recently. As the overhead transmission line has strict requirements on the outer diameter and weight of OPGW, it is of vital importance to perform the physical-layer secure data processing for the distribution network OPGW line with edge computing. To this end, we examine a physical-layer secure distribution network OPGW with edge computing in this article, where there exists one transmitter S, one receiver D, one authorized legitimate monitor LM, and an interfering node I. To better analyze the system performance, we firstly give the definition of the system outage probability, based on the secure data rate. Then, we evaluate the system performance for the distribution network OPGW, by deriving analytical outage probability of secure data processing, to facilitate the system performance evaluation of secure data processing in the entire SNR regime. Finally, we demonstrate some simulation results to validate the analytical results on the physical-layer secure distribution network OPGW line with edge computing.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89575860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm","authors":"Vipan Kusla, Gurbinder Singh Brar","doi":"10.4108/eetsis.v10i3.2680","DOIUrl":"https://doi.org/10.4108/eetsis.v10i3.2680","url":null,"abstract":"INTRODUCTION: Wireless Sensor Network (WSN) has caught the interest of researchers due to the rising popularity of Internet of things(IOT) based smart products and services. In challenging environmental conditions, WSN employs a large number of nodes with limited battery power to sense and transmit data to the base station(BS). Direct data transmission to the BS uses a lot of energy in these circumstances. Selecting the CH in a clustered WSN is considered to be an NP-hard problem.\u0000OBJECTIVES: The objective of this work to provide an effective cluster head selection method that minimize the overall network energy consumption, improved throughput with the main goal of enhanced network lifetime.\u0000METHODS: In this work, a meta heuristic based cluster head selection technique is proposed that has shown an edge over the other state of the art techniques. Cluster compactness, intra-cluster distance, and residual energy are taken into account while choosing CH using multi-objective function. Once the CHs have been identified, data transfer from the CHs to the base station begins. The residual energy of the nodes is finally updated during the data transmission begins.\u0000RESULTS: An analysis of the results has been performed based on average energy consumption, total energy consumption, network lifetime and throughput using two different WSN scenarios. Also, a comparison of the performance has been made other techniques namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Atom Search Optimization (ASO), Gorilla Troop Optimization (GTO), Harmony Search (HS), Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Biogeography Based Optimization (BBO). The findings show that AVOA's first node dies at round 1391 in Scenario-1 and round 1342 in Scenario-2 which is due to lower energy consumption by the sensor nodes thus increasing lifespan of the WSN network.\u0000CONCLUSION: As per the findings, the proposed technique outperforms ABC, ACO, ASO, GTO, HS, WHO, PSO, FA, and BBO in terms of performance evaluation parameters and boosting the reliability of networks over the other state of art techniques.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76029568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Matrix Completion via Successive Low-rank Matrix Approximation","authors":"Jin Wang, Z. Mo","doi":"10.4108/eetsis.v10i3.2878","DOIUrl":"https://doi.org/10.4108/eetsis.v10i3.2878","url":null,"abstract":"In this paper, a successive low-rank matrix approximation algorithm is presented for the matrix completion (MC) based on hard thresholding method, which approximate the optimal low-rank matrix from rank-one matrix step by step. The algorithm enables the distance between the matrix with the observed elements and the projection on low-rank manifold to be minimum. The optimal low-rank matrix with observed elements is obtained when the distance is zero. In theory, convergence and convergent error of the new algorithm are analyzed in detail. Furthermore, some numerical experiments show that the algorithm is more effective in CPU time and precision than the orthogonal rank-one matrix pursuit(OR1MP) algorithm and the augmented Lagrange multiplier (ALM) method when the sampling rate is low.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77185995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online Document Transmission and Recognition of Digital Power Grid with Knowledge Graph","authors":"Yuzhong Zhou, Zhèng-Hóng Lin, Liang-Jung Tu, Qiansu Lv","doi":"10.4108/eetsis.v10i3.2831","DOIUrl":"https://doi.org/10.4108/eetsis.v10i3.2831","url":null,"abstract":"Inspired by the ever-developing information technology and scalable information systems, digital smart grid networks with knowledge graph have been widely applied in many practical scenarios, where the online document transmission and recognition plays an important role in wireless environments. In this article, we investigate the online document transmission and recognition of digital power grid with knowledge graph. In particular, we jointly consider the impact of online transmission and recognition based on computing, where the wireless transmission channels and computing capability are randomly varying. For the considered system, we investigate the system performance by deriving the analytical expression of outage probability, defined by the transmission and recognition latency. Finally, we provide some results to verify the proposed studies, and show that the wireless transmission and computing capability both impose a significant impact on the online document transmission and recognition of digital power grid networks.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90823690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}