scholarly journals Supporting Classification of Software Requirements system Using Intelligent Technologies Algorithms

2021 ◽  
Vol 3 (11) ◽  
pp. 32-39
Author(s):  
Ashraf Abdulmunim Abdulmajeed Althanoon ◽  
Younis S Younis
Keyword(s):  
System Development ◽  
Regular Structure ◽  
Software Requirements ◽  
Learning Approaches ◽  
Functional Requirements ◽  
Suggested Model ◽  
Software Developers ◽  
The Many ◽  
Crucial Part

The important first stage in the life cycle of a program is gathering and analysing requirements for creating or developing a system. The classification of program needs is a crucial step that will be used later in the design and implementation phases. The classification process may be done manually, which takes a lot of time, effort, and money, or it can be done automatically using intelligent approaches, which takes a lot less time, effort, and money. Building a system that supports the needs classification process automatically is a crucial part of software development. The goal of this research is to look into the many automatic classification approaches that are currently available. To assist researchers and software developers in selecting the suitable requirement categorization approach, those requirements were divided into functional and non-functional requirements. since natural language is full of ambiguity and is not well defined, and has no regular structure, it is considered somewhat variable. This paper presents machine requirement classification where system development requirements are categorized into functional and non-functional requirements by using two machine learning approaches. During this research paper, MATLAB 2020a was used, as well as the study's results indicate When applying Multinomial Naive Bayes technology, the model achieves the highest accuracy of 95.55 %,93.09 % sensitivity, and 96.48 % precision, However, when using Logist Regression, the suggested model has a classification accuracy of 91.23 %,91.54 % sensitivity, and 94.32 % precision.

Object-Oriented Modeling with Ontologies Around: A Survey of Existing Approaches

2018 ◽  
Vol 28 (11n12) ◽  
pp. 1775-1794 ◽  
Author(s):  
Selena Baset ◽  
Kilian Stoffel
Keyword(s):  
Object Oriented ◽  
Software Developers ◽  
The Many

Despite the many integration tools proposed for mapping between OWL ontologies and the object-oriented paradigm, developers are still reluctant to incorporate ontologies into their code repositories. In this paper we survey existing approaches for OWL-to-OOP mapping trying to identify reasons for this shy adoption of ontologies among conventional software developers. We present a classification of the surveyed approaches and tools based on their technical characteristics and their resulting artifacts. We discuss further potential reasons beyond what have been addressed in the literature before finally providing our own reflection and outlook.

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

Future Directions

2020 ◽  
pp. 476-479
Author(s):  
Marc N. Potenza ◽  
Kyle A. Faust ◽  
David Faust
Keyword(s):  
Technology Development ◽  
Digital Technologies ◽  
World Health ◽  
International Classification Of Disease ◽  
Levels Of Use ◽  
Pornography Use ◽  
Negative Impacts ◽  
Mental Health Treatments ◽  
The Many

As digital technology development continues to expand, both its positive and negative applications have also grown. As such, it is essential to continue gathering data on the many types of digital technologies, their overall effects, and their impact on public health. The World Health Organization’s inclusion of Gaming Disorder in the eleventh edition of the International Classification of Disease (ICD-11) indicates that some of the problematic effects of gaming are similar to those of substance-use disorders and gambling. Certain behaviors easily engaged in via the internet may also lead to compulsive levels of use in certain users, such as shopping or pornography use. In contrast, digital technologies can also lead to improvements in and wider accessibility to mental health treatments. Furthermore, various types of digital technologies can also lead to benefits such as increased productivity or social functioning. By more effectively understanding the impacts of all types of digital technologies, we can aim to maximize their benefits while minimizing or preventing their negative impacts.

scholarly journals Classification of Oil Palm Female Inflorescences Anthesis Stages Using Machine Learning Approaches

2020 ◽  
Author(s):  
Mamehgol Yousefi ◽  
Azmin Shakrine ◽  
Samsuzana bt. Abd Aziz ◽  
Syaril Azrad ◽  
Mohamed Mazmira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oil Palm ◽  
Learning Approaches

scholarly journals System development for its disclosure of Portuguese digital type design

2016 ◽  
Author(s):  
Vítor Quelhas ◽  
Vasco Branco ◽  
Rui Mendonça
Keyword(s):  
Early Stage ◽  
System Development ◽  
Complete Agreement ◽  
Collaborative System ◽  
Test And Evaluation ◽  
Usability Tests ◽  
Type Design ◽  
Third Stage ◽  
Further Development

This study aims to cover the current development of a platform for the disclosure of the Portuguese type design community, since the beginning of the desktop revolution, until today. 
To deepen our understanding, interviews were made to a selected and representative group of type designers from our sample based on several criteria. The interview tested six dimensions: people, processes, products, uses, identity and platform. The results analyzed in the last dimension, through content analysis and quantitative data, lead to the development of an online digital collaborative system – one of our specific objectives. 
Our hypothesis – that the development of a online digital collaborative system would allow further development of knowledge between products, users and authors, as well as, processes and uses – was also corroborated by the interviewers. 
Reviews have been made to reference international online projects to identify their purposes, areas of activity, objectives, mechanisms of interaction, usability and accessibility. This previous research brought together a set of notes that would become essential in the definition and development of our concept. 

The classification of typefaces is a subject of study by researchers and designers, but it is certainly not a topic for complete agreement. Organizing and balancing the content for the database was our first challenge since we were expecting users with good knowledge on the field, but also beginners. Several diagrams were put into test during the early stage of information architecture to better define categories, filters, and sorting methods, as well as users role in the system. The items and categories chosen were redefined in a second stage, and in the third stage hi-fidelity wireframes were produced, to concentrate on design aspects and decisions, and put the system into test and evaluation. 

The current results on the platform development, with the improvements made through several user tests, evaluations and refinements undertaken in all phases of the project have been crucial. We are expecting to run some pilot tests, as well as usability tests prior to the full implementation to further improve the system and meet the expectations.DOI: http://dx.doi.org/10.4995/IFDP.2016.3351

scholarly journals Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dennie te Molder ◽  
Wasin Poncheewin ◽  
Peter J. Schaap ◽  
Jasper J. Koehorst
Keyword(s):  
Machine Learning ◽  
Plant Pathogens ◽  
De Novo ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Enabling Factors ◽  
Essential Step ◽  
The World ◽  
Genome Content

Abstract Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Results Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Conclusion The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

scholarly journals Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant

2021 ◽  
Vol 11 (22) ◽  
pp. 10713
Author(s):  
Dong-Gyu Lee
Keyword(s):  
Real Time ◽  
Computational Efficiency ◽  
Autonomous Driving ◽  
Learning Approaches ◽  
Practical Applications ◽  
Task Learning ◽  
Lane Line ◽  
High Level ◽  
Time Autonomous

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder-decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.

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