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Single and Multi-Documents Text Summarization Technologies for Natural Language Processing: a Systematic Review on Method and Dataset

Single and Multi-Documents Text Summarization Technologies f...

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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Sukmandhani, Arief Agus Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Bina Nusantara University Binus Online Learning Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665468763

The summarization technologies have been increasingly used in recent decades. Those technologies are a very important part of emerging topics in computer science and engineering, such that Natural Language Processing (NLP). Several methods have been used for analysis to get good summary results. There are two types of document summaries: single document summaries and multi-document summaries. Single document summaries aim to extract information from a single document to get new and relevant summary information, while multi-document summaries extract information from multiple documents. This study focuses on the activities of the semantic literature in previous studies to obtain the basis of the widely used base methods and data sets used in this study. Data was collected from Scopus publication sources from 2019 until 2022 Q2 for analysis. Researchers use guidelines from the semantic literature method by using the basis of Kitchenham and Charters as a reference in its design. In this study, there were forty-eight articles obtained from the filtering results of several criteria used, including exclusion and quality assessment. © 2022 IEEE.

关键词： Semantics

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Machine Learning Methods for Predicting the Necessity of Caesareans Section of Childbirth 5

Machine Learning Methods for Predicting the Necessity of Cae...

引用

5th IEEE International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Sunge, Aswan Supriyadi Heryadi, Yaya Abdurachman, Edi Kartowisatro, Iman H. Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty Of Engineering Computer Science Departement - BINUS Graduate Program Computer Science Computer Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9781665401968

In the process of delivery usually the baby comes out of the vagina but under some circumstances a cesarean section is performed. Caesarean section, on the one hand can have short-term and long-term effects for the mother but on the other hand it is a life-saving procedure for both mother and child. The purpose of this study is to predict whether or not a caesarean section is necessary for a pregnant mother using a machine learning classifier model and some features from medical records as input data. In this study, some machine models were explored including Logistic Regression, K-Nearest Neighbor, Random Forest, Decision Tree, Gradient Boosting and Support Vector Machine. The experiment found that Logistic Regression model achieved the highest performance as measured by average accuracy 95%, average precision 86%, average recall 95%, and average F1 86%. © 2021 IEEE.

关键词： Forecasting

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Analysis of Seismic Hazard Prediction Using Non Parametric Conic Multivariate Adaptive Regression Splines (C-Mars) Methods 1

Analysis of Seismic Hazard Prediction Using Non Parametric C...

引用

1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Priyanto, Dadang Zarlis, Muhammad Mawengkang, Herman Efendi, Syahril Graduate Program of Computer Science Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The data mining process requires a data set that can be used in determining a number of specific patterns to gain new knowledge. Large data sets (Big data) require special methods to get effective results. Included in this study are using big data related to the earthquake in Lombok. Earthquake research, especially in Lombok, is needed because Lombok is on three active plates in Indonesia, so that the danger of earthquake damage can be minimized. Earthquake data obtained from the Geophysical Station (BMKG) of Mataram has different characteristics and is complex, an appropriate method is needed, namely by detecting a non-parametric method with the Multivariate Adaptive Regression Spline (MARS). The use of the backward stepwise algorithm with the Conical Quadratic programming (CQP) framework of MARS, referred to as CMARS (Conic Multivariate Adaptive Regression Splines), is used for optimizing the results. The conclusions of this study are 1. A mathematical model with a total of 12 basis functions (BF) has contributed to the prediction analysis of the PGA dependent variable. 2. Contributions of the influence of independent variables on the PGA value are the epicenter distance (R e pi) of 100% and the Magnitude (Mw) of 31.08608%, while the temperature of the incident location (SUHU) of 5.48525% and depth (Depth) of 3,52988%. 3. Acquired areas that haveearthquake hazard levels in the order of the most vulnerable are Malaka, Genggelang, Tegal Maja, Senggigi and Mangsit. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Quadratic programming

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Applying verbal decision analysis to task allocation in distributed development of software 28

Applying verbal decision analysis to task allocation in dist...

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28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016

作者： Filho, Marum Simão Pinheiro, Plácido R. Albuquerque, Adriano B. Graduate Program in Applied Computer Science University of Fortaleza 7 de Setembro College Fortaleza Brazil Graduate Program in Applied Computer Science University of Fortaleza Fortaleza Brazil

ISBN: (纸本)189170639X

The management of distributed software development projects presents many challenges. One of them happens right at the start of the project and consists of the allocation of tasks between remote teams. When allocating a task to a site, the project manager takes into account several factors such as technical knowledge of staff and proximity to the client. The project manager usually takes this decision in a subjective way. The verbal decision analysis is an approach based on solving problems through multi-criteria qualitative analysis, which means it considers the analysis of subjective criteria. This paper describes the application of verbal decision analysis methodologies ORCLASS and ZAPROS III-i to classify and rank the most relevant factors that the project managers should take into account when allocating tasks in projects of distributed development of software.

关键词： Application programs

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Find the Best Rule of Time Series Data Mining with Cluster Analysis 3

Find the Best Rule of Time Series Data Mining with Cluster A...

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3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Buaton, Relita Zarlis, Muhammad Mawengkang, Herman Efendi, Syahril Graduate Program of Computer Science Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Data mining is an analytical process of knowledge discovery in large and complex data sets. Many studies wish to explore data, to find information so that knowledge can be obtained through the grouping process, classification, rules discovery, associations and data mining visualization which shows similarity. Periodic data often occurs in business applications and sciences that has big size, high dimension and continuously updated. The similarity in periodic data is based on several approaches. One of common approaches is to transform periodic series into other domains so that dimensions are reduced, followed by index mechanism. Many studies of time series do not give optimal result because limited to extracting data not able to represent time series and its pattern which is then change into rules. Rules can be found in time series data, but they are still constrained by over fitting and difficult to present. It causes time series data and non linier function of data mining decision can't be optimal. The basic idea in the method proposed is to do periodic discretization for sub-sequential formation. These sub-sequences are grouped through a measure of similarity. The simple rule-finding technique is applied to obtain hidden rules in the temporal pattern. The optimal time series data expected to generate the uncertainty trend, previously unknown and can be used to make decisions or forecasting in the future. © Published under licence by IOP Publishing Ltd.

关键词： Data mining

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Passive Identification of Rogue Industrial Access Points Using a One-Class Machine Learning Model 50

Passive Identification of Rogue Industrial Access Points Usi...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： de Paula, André L.S. Viegas, Eduardo K. Santin, Altair O. Graduate Program in Computer Science Pontifical Catholic University of Parana Brazil

ISBN: (纸本)9781665464543

Access point (AP) security has become increasingly important as wireless local area networks (WLANs) proliferate in industrial environments. Rogue APs are often used by attackers to conduct man-in-the-middle (MiTM) attacks. They can redirect users to malicious servers or do eavesdropping and manipulation of their *** this paper, we propose a novel one-class machine learning model to passively identify rogue APs in industrial environments. The implementation of the model is twofold. First, we passively extract the hardware and software characteristics of the evaluated AP according to its generated messages. This results in a comprehensive feature set that captures both low-level and high-level behaviors of the evaluated ***, we apply a one-class machine learning model to identify APs that significantly deviate from the previously known profile of legitimate APs. The combined evaluation of hardware and software behaviors integrated with an outlier detection scheme to effectively identify rogue APs is the insight of our proposal. We demonstrate the feasibility of our model, achieving an F1 score of 0.89 and a true positive rate of 0.9 in experiments conducted on our new publicly available dataset of 357 unique AP behaviors. © 2024 IEEE.

关键词： Adversarial machine learning

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A Multi-View Android Malware Detection Model Through Multi-Objective Optimization 23

A Multi-View Android Malware Detection Model Through Multi-O...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Fransozi, Philipe Geremias, Jhonatan Viegas, Eduardo K. Graduate Program in Computer Science Pontifical Catholic University of Parana Brazil

ISBN: (纸本)9798350374889

Over the past few years, several highly accurate Machine Learning (ML) techniques have been proposed for Android malware detection. Unfortunately, proposed schemes are rarely used in production, a situation usually caused by their limited generalization capabilities, leading to low accuracies when deployed. This paper proposes a new multi-view Android malware detection model, implemented in two stages. First, we extract multiple feature sets from an analyzed Android application package. The feature sets provide a complementary Android app behavioral vector for the classification task, enhancing the system's generalization. Secondly, we conduct a multi-objective optimization to select the optimal feature subset from each view for subsequent ensemble-based classification. Our proposal's insight is to proactively select each feature subset that simultaneously improves accuracy and reduces processing requirements in a multi-view setting. Experiments on our new dataset, comprising over 40 thousand Android app samples, demonstrated the feasibility of our proposal. Our scheme can improve true-positive rates by an average of 4.4 while demanding only up to 65% of inference processing costs. © 2024 IEEE.

关键词： Android malware

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Software risk management:: a process model and a tool

Software risk management:: a process model and a tool

引用

IFIP Working Conference on Software Engineering Techniques

作者： Kirner, Teren G. Goncalves, Lourdes E. Graduate Program in Computer Science Methodist University of Piracicaba SP Brazil

ISBN: (纸本)0387393870

This paper is concerned with the risks associated with the software development process. A model (GRisk-Modeo is proposed for the management of such risks and a software tool (GRisk-Tool), developed to support the model, is described. Both the method and the tool were created with the participation of senior managers and software engineers of software factories. The model and the tool serve as effective instruments for achieving the continuous improvement of software processes and products.

关键词： Risk management

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Cooperative Observation of Smart Target Agents 9th

Cooperative Observation of Smart Target Agents

引用

9th Brazilian Conference on Intelligent Systems, BRACIS 2020

作者： Araújo, Matheus S. da Silva, Thayanne F. Sampaio, Vinícius A. Melo, Gabriel F. L. Junior, Raimundo J. Ferro da Costa, Leonardo F. Andrade, Joao P. B. de Campos, Gustavo A. L. Graduate Program in Computer Science State University of Ceara Fortaleza Brazil

ISBN: (纸本)9783030613792

The Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) considers two types of robots, observers and targets, in a partially observable 2D environment, in which the task of the observers is to monitor the target robots under a limited radial range of the sensor, minimizing the total time the targets escape observation. The Cooperative Target Observation (CTO), a variant of the CMOMMT problem, considers the environment to be fully observable, where targets cooperate with observers by reporting their locations. These problems are at the center of many problems that occur in real-world wildlife research, crowd social movement, and surveillance situations. Regarding research related to the field of surveillance, this work proposes an approach to the extended CTO problem, what we call the COSTA problem: Cooperative Observation of Smart Target Agents. The approach employs genetic algorithm and recurrent deep neural networks to improve the performance of target robots and, therefore, to improve the research of new decision-making strategies in the context of observer robots. The first results were auspicious, as the average number of target evasion increased, considering the other recent approaches to the problem. © 2020, Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Towards a Feasible Palm Vein Verification Scheme Using Deep Autoencoder and Siamese Networks 23

Towards a Feasible Palm Vein Verification Scheme Using Deep ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Nunes, Mateus Viegas, Eduardo K. Santin, Altair O. Pontifical Catholic University of Parana Graduate Program in Computer Science Brazil

ISBN: (纸本)9798350374889

Palm vein pattern recognition offers a unique personal identification feature. Unfortunately, these techniques typically require a Near Infrared (NIR) camera sensor to extract the individual's venous pattern, challenging their wide deployment. This paper proposes a new feasible palm vein verification scheme using a Deep Autoencoder and a Siamese Network, implemented threefold. First, we capture the individual's palm using a traditional visible spectrum camera sensor and perform preprocessing tasks to correct imprecise positioning, easing palm support accessories requirements. Second, we eliminate NIR sensor requirement by fine-tuning a Deep Autoencoder model to convert images from the visible spectrum to their infrared counterparts. Third, generated images are processed by a lightweight Siamese network using a contrastive loss function for individual verification. Experiments conducted on a publicly available dataset with over a hundred individuals confirmed the feasibility of our proposal. Our scheme reaches up to 0.97 of true-negative rate, with only 0.01 decrease compared to traditional NIR-based approaches. In addition, individual identification can be conducted in less than 6 seconds in a resource-constrained environment thanks to our lightweight model's implementation. © 2024 IEEE.

关键词： Palmprint recognition

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