scholarly journals SENSE: A Flow-Down Semantics-Based Requirements Engineering Framework

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 298
Author(s):  
Kalliopi Kravari ◽  
Christina Antoniou ◽  
Nick Bassiliades
Keyword(s):  
Requirements Engineering ◽  
Critical Issue ◽  
Machine Learning Techniques ◽  
Natural Languages ◽  
Learning Techniques ◽  
Requirement Management ◽  
Traditional Natural ◽  
And Behavior ◽  
Developing System ◽  
Complete Process

The processes involved in requirements engineering are some of the most, if not the most, important steps in systems development. The need for well-defined requirements remains a critical issue for the development of any system. Describing the structure and behavior of a system could be proven vague, leading to uncertainties, restrictions, or improper functioning of the system that would be hard to fix later. In this context, this article proposes SENSE, a framework based on standardized expressions of natural language with well-defined semantics, called boilerplates, that support a flow-down procedure for requirement management. This framework integrates sets of boilerplates and proposes the most appropriate of them, depending, among other considerations, on the type of requirement and the developing system, while providing validity and completeness verification checks using the minimum consistent set of formalities and languages. SENSE is a consistent and easily understood framework that allows engineers to use formal languages and semantics rather than the traditional natural languages and machine learning techniques, optimizing the requirement development. The main aim of SENSE is to provide a complete process of the production and standardization of the requirements by using semantics, ontologies, and appropriate NLP techniques. Furthermore, SENSE performs the necessary verifications by using SPARQL (SPIN) queries to support requirement management.

scholarly journals Predictive Model for Detection of Depression Based on Uncertainty Analysis Methods

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 551
Author(s):  
Alicia Martínez ◽  
Richard Benítez ◽  
Hugo Estrada ◽  
Yasmín Hernández
Keyword(s):  
Older Adults ◽  
Predictive Model ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
World Population ◽  
Novel Technologies ◽  
Learning Techniques ◽  
Problematic Situations ◽  
And Behavior ◽  
Important Disease

: Currently, advances in technology have permitted increases in the life expectancy of older adults. As a result, a large segment of the world population is 60’s years old, and over. Depression is an important disease in older adults is depression, which seriously affects the moods and behavior of elderly. Novel technologies for smart cities allow us to monitor people and prevent problematic situations related to this mental illness. In this paper, we propose a predictive model to automatically detect depression in older adults. The model is based on machine-learning techniques to analyze the data obtained by a sensor that monitores the daily activities of older adults. Also, the model was evaluated obtaining promising results.

scholarly journals Modeling a Competence-based Industry Role Selection System for University Graduates Using Machine Learning

2020 ◽  
Vol 1 (1) ◽  
pp. 15-25
Author(s):  
Fullgence Mwachoo Mwakondo
Keyword(s):  
Machine Learning ◽  
Prior Information ◽  
Naive Bayes ◽  
Machine Learning Techniques ◽  
Selection System ◽  
Object Modeling ◽  
Learning Techniques ◽  
Blind Search ◽  
And Behavior

This paper presents a design of a system for industry role selection, representing both its structure and behavior. Knowing the right industry role that suits a graduate based on their competences on graduation has remained a critical matter for graduates when searching for jobs after graduation. Thousands of university students graduate each year and enter the market to search for jobs that are limited. Searching without prior information on the most appropriate industry role one is suitable for leads to blind search. Blind search not only puts graduates at risk of long-term unemployment and job mismatch but also overloads employers with many applications during job selection. Therefore, this paper addresses 2 objectives: 1) to model the system’s structure, and 2) to design the algorithm for the system’s behavior. Since object-oriented programming is currently the dominant programming paradigm, object modeling technique was selected to model both the system’s structure and the algorithm for the system’s behavior. To realize object modeling and represent the system’s artifacts in a highly simplified form, Unified Modeling Language (UML) was adopted as the standard modeling toolkit. More specifically, UML class diagram was used to represent the structural model of the system where the underlying objects of the model were exactly similar to those of the problem domain. Finally, use case diagram of the UML toolkit was used to represent the system’s behavior in selecting industry role for graduates. To ensure that the system improves performance of its behavior through experience in selecting industry roles for graduates, Machine Learning (ML) algorithm was designed. Two machine learning techniques, naïve Bayes and Support Vector Machines (SVM), were used as the algorithm’s criteria for selecting industry roles for graduates. Experiments to evaluate performance of the system were conducted using data collected from Software Engineering industry domain. The end product was design of an intelligent industry role selection system with relevant structure and behavior to easily work with both in the academia and industry. Findings reveal the system improves performance of its behavior in selecting industry roles for graduates much better under SVM (67%) than naïve Bayes (57%). On the same benchmark dataset, the system recorded better performance (85%) than reported performance (82%) in the benchmark system. These findings will benefit industry by getting evaluation tool for revealing graduate’s suitability for employment which they can use as prior information for decision making when filtering candidates for interview. Besides, this will provide researchers with a digital platform to study and bridge the gap between industry and academia. Lastly, this will attempt to reduce both low job satisfaction and long-term unemployment that is one of the causes of social and economic pain both in Kenya and around the world. This paper has revealed competence based industry role selection system with relevant structure and behavior can improve searching of jobs by providing a fairly accurate prior information. However, this paper recommends testing this approach with other alternative machine learning techniques as well as other alternative industry domains.

An Analytical Study on Machine Learning Techniques

2021 ◽  
pp. 137-157
Author(s):  
Law Kumar Singh ◽  
Pooja ◽  
Hitendra Garg ◽  
Munish Khanna ◽  
Robin Singh Bhadoria
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Ethical Issues ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Behavior Prediction ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
Learning Machine ◽  
And Behavior

The last few months have produced a remarkable expansion in research and deep study in the field of machine learning. Machine learning is a technique in which the set of the methods are used by the computers to make prediction, improve prediction and behavior prediction based on dataset. The learning techniques can be classified as supervised and unsupervised learning. The focus is on supervised machine learning that covers all the predictions problem for which we had the dataset in which the outcome is already known. Some of the algorithm like naive bayes, linear regression, SVM, k-nearest neighbor, especially neural network have gain growth in this area. The classifiers of machine learning are completely unconstrained with the assumptions of statistical and for that they are adapted by complex data. The authors have demonstrated the application of machine learning techniques and its ethical issues.

scholarly journals MQTTset, a New Dataset for Machine Learning Techniques on MQTT

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6578
Author(s):  
Ivan Vaccari ◽  
Giovanni Chiola ◽  
Maurizio Aiello ◽  
Maurizio Mongelli ◽  
Enrico Cambiaso
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Detection System ◽  
Critical Issue ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Detection Systems ◽  
Learning Techniques ◽  
Iot Devices ◽  
Definition Of

IoT networks are increasingly popular nowadays to monitor critical environments of different nature, significantly increasing the amount of data exchanged. Due to the huge number of connected IoT devices, security of such networks and devices is therefore a critical issue. Detection systems assume a crucial role in the cyber-security field: based on innovative algorithms such as machine learning, they are able to identify or predict cyber-attacks, hence to protect the underlying system. Nevertheless, specific datasets are required to train detection models. In this work we present MQTTset, a dataset focused on the MQTT protocol, widely adopted in IoT networks. We present the creation of the dataset, also validating it through the definition of a hypothetical detection system, by combining the legitimate dataset with cyber-attacks against the MQTT network. Obtained results demonstrate how MQTTset can be used to train machine learning models to implement detection systems able to protect IoT contexts.

scholarly journals Minimizing Device-to-Device Variation in the Spectral Response of Portable Spectrometers

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Sunwoong Choi ◽  
Youngsik Kim ◽  
Jae-Hyung Lee ◽  
Hanjong You ◽  
Byung-Jun Jang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spectral Response ◽  
Critical Issue ◽  
Image Sensor ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Device To Device ◽  
Portable Spectrometers ◽  
Spectral Responses

As portable spectrometers have been developed, the research of spectral analysis has evolved from a traditional laboratory-based closed environment to a network-connected open environment. Consequently, its application areas are expanding in combination with machine learning techniques. The device-to-device variation in the spectral response of portable spectrometers is a critical issue in a machine learning-based service scenario since the classification performance is highly dependent on the consistency of spectral responses from each spectrometer. To minimize device-to-device variation, a cuboid prism is employed instead of a combination of mirrors and prism to construct an optical system for the spectrometer. The spectral responses are calibrated to correct pixel shift on the image sensor. Experimental results show that the proposed method can minimize the device-to-device variation in spectral response of portable spectrometers.

scholarly journals Recommender Systems in Requirements Engineering

AI Magazine ◽  
2011 ◽  
Vol 32 (3) ◽  
pp. 81-89 ◽  
Author(s):  
Bamshad Mobasher ◽  
Jane Cleland-Huang
Keyword(s):  
Recommender Systems ◽  
Requirements Engineering ◽  
Requirements Elicitation ◽  
Machine Learning Techniques ◽  
Stakeholder Identification ◽  
Complex Process ◽  
Management Processes ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
International Projects

Requirements engineering in large-scaled industrial, government, and international projects can be a highly complex process involving thousands, or even hundreds of thousands of potentially distributed stakeholders. The process can result in massive amounts of noisy and semistructured data that must be analyzed and distilled in order to extract useful requirements. As a result, many human intensive tasks in requirements elicitation, analysis, and management processes can be augmented and supported through the use of recommender system and machine learning techniques. In this article we describe several areas in which recommendation technologies have been applied to the requirements engineering domain, namely stakeholder identification, domain analysis, requirements elicitation, and decision support across several requirements analysis and prioritization tasks. We also highlight ongoing challenges and opportunities for applying recommender systems in the requirements engineering domain.

scholarly journals Collaboration Robots as Digital Doubles of Person for Communication in Public Life and Space

2019 ◽  
Vol 1 (4) ◽  
pp. 1-11
Author(s):  
Evgeniy Bryndin
Keyword(s):  
Neural Networks ◽  
Social Services ◽  
Service Sector ◽  
Process Management ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Human Machine Interaction ◽  
Digital Twins ◽  
Learning Techniques ◽  
And Behavior

Trans-sectoral digital research of man, nature, society and industrial communication allows to create digital twins of social services. Digital duplicates associated with the service sector are created for intelligent process management. Digital twins of man provide services in the social sphere and in space. Training of digital twins in professional competences is carried out on the basis of communicative associative logic of technological thinking by cognitive methods. The study of the effectiveness of machine learning techniques, allowed the use of an approach that allows the combination of artificial intelligence and cognitive psychology. This approach provided pre-preparation of neural networks from accumulated data using existing behaviors. The approach combines existing scientific theories of human behavior with the flexibility of neural networks to make better decisions made by humans in space and in extreme situations. From a practical point of view, this makes it possible to more accurately determine the behavior of the human digital twin in space and in extreme situations. There are a number of socio-economic issues related to human-machine interaction. Complex technologies are not credible on the part of citizens. The coming years will take to improve safety and standardize the creation, application of digital twins and behavior of robots.

Deep Learning for Social Media Text Analytics

2021 ◽  
pp. 68-91
Author(s):  
Anto Arockia Rosaline R. ◽  
Parvathi R.
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Text Analytics ◽  
Learning Techniques ◽  
The Social ◽  
Social Media Text ◽  
High Quality Information ◽  
Traditional Natural ◽  
Processing Techniques

Text analytics is the process of extracting high quality information from the text. A set of statistical, linguistic, and machine learning techniques are used to represent the information content from various textual sources such as data analysis, research, or investigation. Text is the common way of communication in social media. The understanding of text includes a variety of tasks including text classification, slang, and other languages. Traditional Natural Language Processing (NLP) techniques require extensive pre-processing techniques to handle the text. When a word “Amazon” occurs in the social media text, there should be a meaningful approach to find out whether it is referring to forest or Kindle. Most of the time, the NLP techniques fail in handling the slang and spellings correctly. Messages in Twitter are so short such that it is difficult to build semantic connections between them. Some messages such as “Gud nite” actually do not contain any real words but are still used for communication.

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