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Identifying Non-functional Requirements from Unconstrained Documents using Natural Language Processing and Machine Learning Approaches

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IEEE Access 2021年

作者： Shreda, Qais A. Hanani, Abualsoud A. Software Engineering master program Birzeit University Palestine.

Requirements engineering is the first phase in software development life cycle and it also plays one of the most important and critical roles. Requirement document mainly contains both functional requirements and non-functional requirements. Non-functional requirements are significant to describe the properties and constraints of the system. Early identification of Non-functional requirement has direct impact on the system architecture and initial design decision. Practically, non-functional requirements are extracted manually from the document. This makes it tedious, time-consuming task and prone to various errors. In this paper, we propose an automatic approach to identify and classify non-functional requirements using semantic and syntactic analysis with machine learning approaches from unconstrained documents. We used A dataset of public requirements documents (PURE) that consists of 79 unconstrained requirements documents in different forms. In our approach, features were extracted from the requirement sentences using four different natural language processing methods including statistical and state-of-the-art semantic analysis presented by Google word2vec and bidirectional encoder representations from transformers models. The adopted approach can efficiently classify non-functional requirements with an accuracy between 84% and 87% using statistical vectorization method and 88% to 92% using word embedding semantic methods. Furthermore, by fusing different models trained on different features, the accuracy improves by 2.4% compared with the best individual classifier. CCBY

关键词： Syntactics

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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software Project Management Effectiveness Evaluation Tool 20

Software Project Management Effectiveness Evaluation Tool

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Proceedings of the 9th International Conference on software and Information engineering

作者： Mohamed Ellithey Barghoth Akram Salah Manal A Ismail Master in Software Engineering Program Egyptian E-Learning University Giza Egypt Cairo University Giza Egypt Department of Computer Engineering and Systems Helwan University Helwan Egypt

ISBN: (纸本)9781450377218

Project management is important for the success of a software project. Enhancing software project management effectiveness measurements should lead the project manager to advance practices that contribute to the successful development of software projects. This study aims to enhance project management effectiveness measurements in the direction of increase the likelihood of software project success. An enhanced evaluation model of software project management effectiveness has been proposed in this study. Correspondingly, the model fed into a developed software project management effectiveness evaluation tool. This measurement tool helps software development managers to evaluate, monitor and improve project management effectiveness in software projects. The developed software project management effectiveness evaluation tool has been validated by a feedback survey. Feedback survey participants have been demonstrated the importance of the developed project management effectiveness measurement tool.

关键词： software project management model Measurements of software project management effectiveness software project management effectiveness software project success

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software Project Management Effectiveness Evaluation Tool 9

Software Project Management Effectiveness Evaluation Tool

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9th International Conference on software and Information engineering, ICSIE 2020

作者： Barghoth, Mohamed Ellithey Salah, Akram Ismail, Manal A Master in Software Engineering Program Egyptian E-Learning University Giza Egypt Faculty of Computer and Artificial Intelligence Cairo University Giza Egypt Department of Computer Engineering and Systems Faculty of Engineering Helwan University Helwan Egypt

ISBN: (纸本)9781450377218

关键词： Project management

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Performance analysis by working day in the placement of techano brick

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AIP Conference Proceedings 2022年第1期2574卷

作者： Angel Guillermo Montaño Herrera Jose David Quizhpe Campoverde Marco Avila-Calle Marlon Lenin Aguilar Gomez 1Catholic University of Cuenca Master’s Program in Construction with a major in Sustainable Construction management camino a Patamarca y Cojimíes – Uncovía Cuenca Ecuador 2Catholic University of Cuenca Academic Unit of Engineering Industry and Construction Av. De las Américas y General Torres esquina s/n Cuenca Ecuador 3Independed profesional Software engineer Cuenca – Ecuador

Considering the current economic and social situation that we are going through, which forces the construction companies of the city of Cuenca to optimize costs that allow them to be competitive in the current economic situation. Considering the different scenarios that can occur in a high-rise building, several of them are analysed, which allow the gradual increase in the performance of workers in the placement of techano brick, with the aim of improving the construction processes that are carried out in the different works of the city of Cuenca - Ecuador, which is where the study is carried out, with the definition of specific tasks of the workers and with the help of mechanical and seasonal equipment that directly affect the sequential increase of the units placed by day. The information is gathered from surveys conducted among construction residents and builders in charge of staff in the city.

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Transient Thermal Analysis of Welding Fixtures Design 2

Transient Thermal Analysis of Welding Fixtures Design

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2nd International Conference on Eco engineering Development 2018, ICEED 2018

作者： Anual, M.T. Sahroni, T.R. Akmal, S. Nasution, F.A. Faculty of Manufacturing Engineering Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Faculty of Manufacturing Engineering Innovative Software System and Services Research Group Centre for Advanced Computing Technology Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia

This paper presents the design and analysis of welding fixtures based on transient thermal analysis. Welding fixtures is designed to hold and support the workpiece securely during the welding process. The heat transformation from the workpiece could lead the temperature rises and impacts to the welding fixtures. Therefore, the study introduces the development of welding fixtures as one of a safety measure prevents the welder from injury while handling welding process. The conceptual design of welding fixtures and material were applied to conduct the simulation. Transient thermal analysis was performed on temperature and total heat flux by using computer aided engineering software. The result shows the analysis of equivalent stress, total deformation, factors of safety, fatigue life and fatigue damage. Furthermore, the equivalent stress failure theory used to predict yielding in welding fixture design. The design of welding fixtures and material selection were proposed to hold and support the workpiece securely. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Heat flux

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Transient Thermal Analysis of Welding Fixtures Design

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IOP Conference Series: Earth and Environmental Science 2018年第1期195卷

作者： M T Anual T R Sahroni S Akmal F A Nasution Faculty of Manufacturing Engineering Universiti Teknikal Malaysia Melaka Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia 11480 Faculty of Manufacturing Engineering Innovative Software System & Services Research Group Centre for Advanced Computing Technology Universiti Teknikal Malaysia Melaka Hang Tuah Jaya 76100 Durian Tunggal Melaka Malaysia. Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480

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Teeth labeling from CBCT data using the Circular Hough Transform

Teeth labeling from CBCT data using the Circular Hough Trans...

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International Symposium on Fundamentals of Electrical engineering (ISFEE)

作者： Ionel-Bujorel Pavaloiu Andrei Vasilateanu Nicolae Goga Iuliana Marin Andrei Ungar Ion Pătrascu Department of Engineering in Foreign Languages University POLITEHNICA of Bucharest Software Engineering Master Program University POLITEHNICA of Bucharest Faculty of Dentistry “Carol Davila” University of Medicine and Pharmacy

In this paper, we explore the possibilities to detect and label straightforward the teeth in Cone Beam Computed Tomography images, without performing complex segmentation procedures on teeth. We are using the Circular Hough Transform (CHT) to find the teeth positions, segmentation using intensity level to determine the mandible and deformable templates to find the best fit position for the teeth on the mandible. The dental arch is split into 16 regions and the teeth are labeled by their position in a given region.

关键词： Conferences Biomedical imaging IEEE members Automobiles Indexes Electrical engineering Control systems

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Various forms of indexing HDMR for modelling multivariate classification problems

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AIP Conference Proceedings 2014年第1期1637卷 18-25页

作者： Çağrı Aksu M. Alper Tunga Bahçeşehir University Information Technologies Master Program Beşiktaş 34349 İstanbul Turkey Bahçeşehir University Software Engineering Department Beşiktaş 34349 İstanbul Turkey

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled. In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.

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