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2024 IEEE International Conference on computing, ICOCO 2024

作者： Ramasamy, Rosaan Lim, Tek Yong Antdragon Sdn Bhd Negeri Sembilan Malaysia Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9798331530303

Non-functional requirements (NFRs) are often poorly documented and inadequately integrated into the software development process resulting in discrepancies between expected and actual system quality. This gap leads to a number of issues like requirement ambiguity, ineffective stakeholder communication and inadequate validation which severely impact project outcomes. This systematic review addresses these challenges by investigating the state of NFRs management in requirements engineering. The review identifies predominant techniques such as automated tools and document analysis for eliciting NFRs while also highlighting current challenges and effective validation practices, including inspections, reviews and user feedback. Future research should develop comprehensive frameworks combining technology with human-centered approaches to address these challenges. ©2024 IEEE.

关键词： I. INTRODUCTION non-functional requirement requirements engineering systematic review

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Web Service Prediction and Composition Based on Harris Hawks Optimization and Deep Learning

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SN Computer Science 2025年第5期6卷 1-25页

作者： Elhoseny, Mohamed Abdel-salam, Mahmoud El-hasnony, Ibrahim M. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Faculty of Computers and Information Science Mansoura University Mansoura Egypt

QoS-aware service composition suffers from many problems due to the uncertainties of its working environment. Besides, the many candidate services for service composition consume more processing and execution time. This paper presents a four-phases framework for reliable dynamic Web service composition. The preprocessing phase helps to reduce the percentage of missing values of the dataset. Then, clustering phase applies self-organizing maps (SOM) algorithm to cluster the QoS data to reduce data sparsity and cold-start problems of web services. Then, the prediction phase predicts uncertain QoS values of services using an optimized LSTM deep learning model. In the composition module, an improved hybrid optimization algorithm IHHO-SWO is the proposed integrating the Spider Wasp Optimizer (SWO) and is applied to generate a composite service that fits the end user requirements. An exhaustive experimental and statistical study validates the superior performance of the suggested framework. The study reveals that the framework is able to successfully deal with service prediction and composition challenges such as outlier handling, cold start, and data sparsity, lowering these problems considerably. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Classification Data analysis Harris Hawk Optimization Prediction model Service composition Spider Wasp Optimization

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Conceptualizing Hybrid Descriptive-Semantic Techniques to Enhance Sentiment Analysis of YouTube Reviews: Malaysian Island Contexts 12

Conceptualizing Hybrid Descriptive-Semantic Techniques to En...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Bidin, Nurain Batrisyia Binti Samah, Khyrina Airin Fariza Abu Aminuddin, Raihah Binti Universiti Teknologi MARA Melaka Branch College of Computing Informatics and Mathematics Jasin Campus Melaka Malaysia

ISBN: (纸本)9798350391398

Island tourism is a prominent sector in Malaysia that attracts tourists from various countries. The popularity of Malaysia's islands as vacation destinations necessitates thorough research and analysis to identify the ideal vacation spots. With the rise of YouTube as a communication and expression platform, it has become a valuable source of information where users share their perspectives and ideas through video reviews. Following that, Sentiment Analysis (SA) comprehends the context of any word and the emotions expressed in a sentence in order to determine relevant topics by analysing user preferences and trends. Thus, this research aims to enhance SA on YouTube reviews to gain extensive perspectives and insights from users, focusing on Malaysia's islands. This research proposes two techniques combining descriptive and semantic analysis to enhance performance in SA: Aspect-Based Sentiment Analysis (ABSA) for sentiment labelling and Support Vector Machine (SVM) for sentiment classification. Furthermore, this research also involved two phases in developing the comparative technique: 1) problem assessment and research study and 2) techniques for descriptive and semantics. As a result, this research outcome will support decision-making processes, improve the island tourism experience, and contribute to the overall growth of the Malaysian tourism industry. © 2024 IEEE.

关键词： Semantics

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Fuzzy Topsis with Ratings Based on Sub-Criteria for Selection of Supplier 5

Fuzzy Topsis with Ratings Based on Sub-Criteria for Selectio...

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Razak, Samsiah Abdul Ramli, Nazirah Azmi, Muhammad Ikmal Hakim Nor Nor, Muhammad Ikmal Mohd Noreddie, Hadizul Alif Hakimi College of Computing Informatics and Mathematics Universiti Teknologi Mara Cawangan Perak Kampus Tapah Tapah Road Perak35400 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Cawangan Pahang Kampus Jengka Pahang Bandar Jengka26400 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Cawangan Negeri Sembilan Kampus Seremban Negeri Sembilan Seremban70300 Malaysia

ISBN: (纸本)9798331528553

The success of a company greatly depends on the supplier's selection. Similar to traditional approaches, the process of supplier selection relies on human assessment, which is inherently subjective and vague. The fuzzy TOPSIS has been successfully applied in many multi-criteria decision-making processes in uncertain environments, including the selection of suppliers. The majority of fuzzy TOPSIS methods take into account the ratings of alternatives for each primary criterion. However, there is still a shortage of studies that utilise ratings based on sub-criteria. This paper aims to determine the optimal criteria for selecting a supplier in the Information Technology (IT) business, with the ratings assigned according to sub-criteria related to benefits and costs. Furthermore, the study aims to determine the finest supplier meeting the established criteria within the IT industry. There are two phases involved, where the first phase consists of converting the benefit and cost sub-criteria into the main criteria using the normalisation and averaging method, and the second phase involves the Closeness Coefficient (CC), Fuzzy Positive Ideal Solution (FPIS), Fuzzy Negative Ideal Solution (FNIS) and the ranking using the centroid method. The outcomes indicated that among the four accessible suppliers, Supplier 1 (S1) is the best option, followed by Supplier 2 (S2), Supplier 4 (S4) and Supplier 3 (S3). The study also determines the criteria that need to be prioritised, which are product performance and service performance. Fuzzy TOPSIS, which has ratings based on benefit and cost sub-criteria, has the potential to automate the procedure and address vagueness or uncertainty during the selection process. © 2024 IEEE.

关键词： Costs

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Leveraging Internet of Things for Upstream and Downstream Real-Time Monitoring in Flood-Prone Areas: A Case Study in Brunei Darussalam 6

Leveraging Internet of Things for Upstream and Downstream Re...

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6th IEEE Symposium on Computers and informatics, ISCI 2024

作者： Ismail, Rasyidah Suhaili, Wida Patchmuthu, Ravi Shams, Shahriar Universiti Teknologi Brunei School of Computing and Informatics Brunei Universiti Teknologi Brunei Faculty of Engineering Brunei

ISBN: (纸本)9798350353853

This study focuses and addresses the critical need for effective flood early detection and water quality assessment, particularly in regions prone to flooding in Brunei Darussalam. This is due to the alteration in weather patterns, commonly known as climate change. The research emphasizes the comprehensive understanding of the significance of pre-deployment characterization studies, which delve into various aspects such as hydrological patterns, water flow dynamics, sediment transport, and ecological conditions. This enables the development and optimal deployment of robust sensor networks and operation of Internet of Things (IoT) sensors capable of withstanding the environmental stressors by understanding the specific challenges posed by the river environment, such as variations in water levels, weather patterns and sediment concentrations. The implementation of IoT sensors in the river allows the collection and monitoring of real-time data on water quality parameters, water level, water flow dynamics and weather conditions. The use of mobile networks aims to transmit data through the gateway to display the data on a dashboard for visualization and generate an alert triggering an alarm for prompt evacuation and mitigation efforts. The integration of solar-powered IoT sensors with mobile networks offers a viable and sustainable approach for early flood detection and anomalies in water quality in river ecosystems leading to the improvement for community resilience and minimization of negative impacts floods have to infrastructure for efficient disaster management strategies around flood-prone regions in Brunei Darussalam. © 2024 IEEE.

关键词： Rivers

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Medical Data Imputation by Hybrid Optimized Fuzzy C-Means and Ant Lion Optimization Algorithm 22

Medical Data Imputation by Hybrid Optimized Fuzzy C-Means an...

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22nd IEEE Student Conference on Research and Development, SCOReD 2024

作者： Dzulkalnine, Mohamad Faiz Samah, Khyrina Airin Fariza Abu Mahzan, Sulaiman Universiti Teknologi MARA Melaka Branch College of Computing Informatics and Mathematics Jasin Campus Melaka Malaysia

ISBN: (纸本)9798331510077

This paper uses a nature-inspired optimization algorithm called Ant Lion Optimization (ALO) to optimize the parameters of an imputation method, Fuzzy c-means (FCM). FCM is severely affected by the selection of parameters c and m, which control the cluster number and fuzziness variable, respectively. Without the appropriate parameters selection, the prediction provided by FCM will result in low accuracy imputation. The traditional trial and error approach, which combines the parameters, is not reliable and prone to error as the value of c can range from 2 to the number of data while the value of m can range from 1 to infinity. Thus, a hybrid with a reliable optimization algorithm that can escape local optima and have fast computational time is appropriate. As such, a hybrid with an ALO optimization algorithm is proposed, inspired by the antlion hunting mechanism in nature. The proposed hybrid FCM-ALO was tested on three medical datasets: fertility, diabetes, and heart disease. Five missing rates are also used to test the proposed method's reliability in increasing the rate of missing values further. The experimental results show that FCM-ALO produced a low error rate in Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) in all the datasets in all missing rates. Numerical and comparative results show that the proposed hybrid optimized imputation method has good convergence ability and better imputation quality in low computational time. © 2024 IEEE.

关键词： Mean square error

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Evaluate Student Confidence Through Accuracy Enhancement of Confidence-Based Assessment: A Conceptual Model Development 11

Evaluate Student Confidence Through Accuracy Enhancement of ...

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11th IEEE Conference on Systems, Process and Control, ICSPC 2023

作者： Azharludin, Nur Maisarah Nor Samah, Khyrina Airin Fariza Abu Zain, Jasni Mohamad College of Computing Informatics and Mathematics Universiti Teknologi Mara Melaka Branch Jasin Campus Melaka Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Shah Alam Malaysia

ISBN: (纸本)9798350340860

Students' confidence level when responding to questions is significant in assessing their academic performance. Confidence can be defined as a psychological state by a sense of assurance and positive feelings towards one's actions or beliefs. Confidence-based assessment (CBA) evaluates a student's confidence level or expectation concerning their response to identify their actual knowledge. Nevertheless, it should be noted that including imprecise self-learning insights in CBA might lead to a lack of dependability and precision in its measurements. Consequently, this can have a negative impact on the overall effectiveness of CBA. While machine learning (ML) has demonstrated the ability to achieve high accuracy in classification and prediction tasks, it is important to acknowledge the presence of an overfitting issue associated with these methods. Thus, this research aims to describe the steps involved in the conceptual model development to enhance CBA accuracy. The indicators that play significant roles in CBA for developing the conceptual model are multiple choice question correctness answers and selection of confidence level indicators (full or partial). This research involved three phases in developing the proposed conceptual model: 1) problem assessment and research study, 2) indicators of CBA, and 3) ensemble learning approaches to enhance CBA accuracy. Therefore, this proposed conceptual model overcomes the overfitting problems commonly encountered in ML applications. As a result, student's performance can be displayed as the educator can evaluate their teaching methods, and students can recognize the areas that they need to improve. © 2023 IEEE.

关键词： Students

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E-Serve for Environment Monitoring Using LoRaWAN 10

E-Serve for Environment Monitoring Using LoRaWAN

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10th International Conference on Electrical and Electronics Engineering, ICEEE 2023

作者： Khalid, Nor Azimah Jamaludin, Muhammad Alif Abdullah, Shapina Bt College of Computing Informatics and Media Universiti Teknologi Mara Shah Alam Malaysia

ISBN: (纸本)9798350304299

The number of forest fires have been increasing rapidly in different parts of the world and has caused a major public concern which can jeopardize our ecosystem. By 2100, it is estimated that over 49% of our forests will be exposed to pollution and this can cause the world climate to worsen. This has led to an increasing demand in environment monitoring applications. The use of remote sensing and development of weather stations have several challenges in terms of coverage and cost. However, wireless sensor networks technologies and the existence of Internet of Things (IoT) has made better communications in monitoring environment in a large area. In this paper, a prototype, called e-Serve, implements the Long Range (LoRa) technology (a branch of IoT), to collect and monitor the environment. The prototype will be equipped with a LoRa node consisting of sensors to measure the temperature, humidity, and air quality. Gathered data will be sent to the server and alert the administrator for any abnormalities in order to deter the environmental risk such as forest fires and pollution. © 2023 IEEE.

关键词： Internet of things

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A Survey of an Adaptation of Non-Immersive Virtual Reality and Leap Motion Controller in Playing Kenong: A Cultural Preservation 11

A Survey of an Adaptation of Non-Immersive Virtual Reality a...

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11th IEEE Conference on Systems, Process and Control, ICSPC 2023

作者： Zulazwar, Zamira Izzati Nasaruddin, Nor Intan Shafini Mohd Rum, Siti Fatimah Awang, Norbahiyah Abu Bakar, Nor Fauziah College of Computing Informatics and Mathematics Universiti Teknologi Mara Malacca Jasin Campus Melaka Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Johor Segamat Campus Johor Malaysia

ISBN: (纸本)9798350340860

This study explicitly discusses the alternative platform to learn and play Gamelan in preserving one of the unique cultural instruments in the archipelago. A Gamelan set commonly consists of seven instruments, but this study focused only on Kenong. Due to Kenong's limited mobility and accessibility, users must often attend physical lessons to practice. The kenong set is heavy and difficult to obtain because it must be customized and imported from different countries. As a result, this study proposed a Virtual Reality application for users to learn and practice Kenong. The Leap Motion Controller was utilized in the program to simulate the real Kenong experience. The ADDIE model is the approach used as a guideline to design and develop the Virtual Reality application. The System Usability Scale with an adjective rating scale is applied to the 113 respondents to measure the application's usability. The result shows that this application's usability is satisfactory and above the standard, which obtained 83.5%. Furthermore, more enhanced features are suggested in future work by including more songs and additional Gamelan instruments like Bonang. © 2023 IEEE.

关键词： Virtual reality

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Bias analysis of AI models for undergraduate student admissions

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Neural computing and Applications 2025年第12期37卷 7785-7795页

作者： Van Busum, Kelly Fang, Shiaofen Department of Computer Science and Software Engineering Butler University IndianapolisIN United States Computing and Engineering Indiana University Luddy School of Informatics Indiana University Indianapolis IndianapolisIN United States

Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors Van Busum and Fang (Proceedings of the 38th ACM/SIGAPP Symposium on Applied computing, 2023) to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a given student would be directly admitted into the School of Science under various scenarios at a large urban research university. During this time, submission of standardized test scores as part of a student’s application became optional which led to interesting questions about the impact of standardized test scores on admission decisions. We developed and analyzed AI models to understand which variables are important in admissions decisions, and how the decision to exclude test scores affects the demographics of the students who are admitted. We then evaluated the predictive models to detect and analyze biases these models may carry with respect to three variables chosen to represent sensitive populations: gender, race, and whether a student was the first in his/her family to attend college. We also extended our analysis to show that the biases detected were persistent. Finally, we included several fairness metrics in our analysis and discussed the uses and limitations of these metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Students

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