arXiv subjects

Mohammed A. Alsuwaiket

Publications and source records attributed to Mohammed A. Alsuwaiket.

7 recordsLinked to original sources

arXiv2022

Naming the Identified Feature Implementation Blocks from Software Source Code

Identifying software identifiers that implement a particular feature of a software product is known as feature identification. Feature identification is one of the most critical and popular processes performed by software engineers during software maintenance activity. However, a meaningful name must be assigned to the Identified Feature Implementation Block (IFIB) to complete the feature identification process. The feature naming process remains a challenging task, where the majority of existing approaches manually assign the name of the IFIB. In this paper, the approach called FeatureClouds was proposed, which can be exploited by software developers to name the IFIBs from software code. FeatureClouds approach incorporates word clouds visualization technique to name Feature Blocks (FBs) by using the most frequent words across these blocks. FeatureClouds had evaluated by assessing its added benefit to the current approaches in the literature, where limited tool support was supplied to software developers to distinguish feature names of the IFIBs. For validity, FeatureClouds had applied to draw shapes and ArgoUML software. The findings showed that the proposed approach achieved promising results according to well-known metrics in terms of Precision and Recall.

cs.SE↗

arXiv2022

Detecting commonality and variability in use-case diagram variants

The use-case diagram is a software artifact. Thus, as with any software artifact, the use-case diagrams change across time through the software development life cycle. Therefore, several versions of the same diagram are existed at distinct times. Thus, comparing all use-case diagram variants to detect common and variable use-cases becomes one of the main challenges in the product line reengineering field. The contribution of this paper is to suggest an automatic approach to compare a collection of use-case diagram variants and detect both commonality and variability. In our work, every use-case represents a feature. The proposed approach visualizes the detected features using formal concept analysis, where common and variable features are introduced to software engineers. The proposed approach was applied on a mobile media case study to be validated. The findings confirm the importance and the performance of the suggested approach as all common and variable features were precisely detected via formal concept analysis and latent semantic indexing.

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arXiv2021

Constructing a software requirements specification and design for electronic IT news magazine system

Requirements engineering process intends to obtain software services and constraints. This process is essential to meet the customer's needs and expectations. This process includes three main activities in general. These are detecting requirements by interacting with software stakeholders, transferring these requirements into a standard document, and examining that the requirements really define the software that the client needs. Functional requirements are services that the software should deliver to the end-user. In addition, functional requirements describe how the software should respond to specific inputs, and how the software should behave in certain circumstances. This paper aims to develop a software requirements specification document of the electronic IT news magazine system. The electronic magazine provides users to post and view up-to-date IT news. Still, there is a lack in the literature of comprehensive studies about the construction of the electronic magazine software specification and design in conformance with the contemporary software development processes. Moreover, there is a need for a suitable research framework to support the requirements engineering process. The novelty of this paper is the construction of software specification and design of the electronic magazine by following the Al-Msie'deen research framework. All the documents of software requirements specification and design have been constructed to conform to the agile usage-centered design technique and the proposed research framework. A requirements specification and design are suggested and followed for the construction of the electronic magazine software. This study proved that involving users extensively in the process of software requirements specification and design will lead to the creation of dependable and acceptable software systems.

cs.SE↗

arXiv2020

Intelligent Risk Alarm for Asthma Patients using Artificial Neural Networks

Asthma is a chronic disease of the airways of the lungs. It results in inflammation and narrowing of the respiratory passages, which prevents air flow into the airways and leads to frequent bouts of shortness of breath with wheezing accompanied by coughing and phlegm after exposure to inhalation of substances that provoke allergic reactions or irritation of the respiratory system. Data mining in healthcare system is very important in diagnosing and understanding data, so data mining aims to solve basic problems in diagnosing diseases due to the complexity of diagnosing asthma. Predicting chemicals in the atmosphere is very important and one of the most difficult problems since the last century. In this paper, the impact of chemicals on asthma patient will be presented and discussed. Sensor system called MQ5 will be used to examine the smoke and nitrogen content in the atmosphere. MQ5 will be inserted in a wristwatch that checks the smoke and nitrogen content in the patients place, the system shall issue a warning alarm if this gas affects the person with asthma. It will be based on the Artificial Neural Networks (ANN) algorithm that has been built using data that containing a set of chemicals such as carbon monoxide, NMHC (GT) acid gas, C6H6 (GT) Gasoline, NOx (GT) Nitrogen Oxide, and NO2 (GT) Nitrogen Dioxide. The temperature and humidity will be also used as they can negatively affect asthma patient. Finally, the rating model was evaluated and achieved 99.58% classification accuracy.

eess.SP↗

arXiv2020

Refining Student Marks based on Enrolled Modules Assessment Methods using Data Mining Techniques

Choosing the right and effective way to assess students is one of the most important tasks of higher education. Many studies have shown that students tend to receive higher scores during their studies when assessed by different study methods which include units that are fully assessed by varying the duration of study or a combination of courses and exams than by exams alone. Many Educational Data Mining studies process data in advance through traditional data extraction, including the data preparation process. In this paper, we propose a different data preparation process by investigating more than 230000 student records for the preparation of scores. The data have been processed through diverse stages in order to extract a categorical factor through which students module marks are refined during the data preparation stage. The results of this work show that students final marks should not be isolated from the nature of the enrolled module assessment methods. They must rather be investigated thoroughly and considered during EDM data preprocessing stage. More generally, educational data should not be prepared in the same way normal data are due to the differences in data sources, applications, and error types. The effect of Module Assessment Index on the prediction process using Random Forest and Naive Bayes classification techniques were investigated. It was shown that considering MAI as attribute increases the accuracy of predicting students second year averages based on their first year averages.

cs.CY↗

arXiv2020

Mosques Smart Domes System using Machine Learning Algorithms

Millions of mosques around the world are suffering some problems such as ventilation and difficulty getting rid of bacteria, especially in rush hours where congestion in mosques leads to air pollution and spread of bacteria, in addition to unpleasant odors and to a state of discomfort during the pray times, where in most mosques there are no enough windows to ventilate the mosque well. This paper aims to solve these problems by building a model of smart mosques domes using weather features and outside temperatures. Machine learning algorithms such as k Nearest Neighbors and Decision Tree were applied to predict the state of the domes open or close. The experiments of this paper were applied on Prophet mosque in Saudi Arabia, which basically contains twenty seven manually moving domes. Both machine learning algorithms were tested and evaluated using different evaluation methods. After comparing the results for both algorithms, DT algorithm was achieved higher accuracy 98% comparing with 95% accuracy for kNN algorithm. Finally, the results of this study were promising and will be helpful for all mosques to use our proposed model for controlling domes automatically.

cs.CY↗

arXiv2018

A path to AI

To build a safe system that would replicate and perhaps transcend human-level intelligence, three basic modules: objective, agent, and perception are proposed for development. The objective module would ensure that the system acts in humanity's interest, not against it. It would have two components: a network of machine learning agents to address the problem of value alignment and a distributed ledger to propose a mechanism to mitigate the existential threat. The agent module would further develop the Dyna concept and benefit from a treatise in sociology to build the missing link of artificial general intelligence - a world simulator. The perception module would estimate the state of the world and benefit from existing machine learning algorithms enhanced by a new paradigm in hardware design - a quantum computer. This paper describes a way in which such a system could be built, analyzing the current state of the art and providing alternative directions for research rather than concrete, industry-ready solutions.

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