IAES International Journal of Artificial Intelligence (IJ-AI)最新文献

Comparative analysis of fuzzy multi-criteria decision-making methods for quality of service-based web service selection 基于服务质量的网络服务选择的模糊多标准决策方法比较分析

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1408-1419

Paul Aazagreyir, Peter Appiahene, Obed Appiah, Samuel Boateng

{"title":"Comparative analysis of fuzzy multi-criteria decision-making methods for quality of service-based web service selection","authors":"Paul Aazagreyir, Peter Appiahene, Obed Appiah, Samuel Boateng","doi":"10.11591/ijai.v13.i2.pp1408-1419","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1408-1419","url":null,"abstract":"This research aims to compare and analyze the effectiveness of four popular fuzzy multi-criteria decision-making methods (FMCDMMs) for quality of service (QoS)-based web service selection. These methods are fuzzy DEMATEL (FD), fuzzy TOPSIS (FT), fuzzy VIKOR (FV), and fuzzy PROMETHEE (FP), including three ranking versions of FV. We assess the ranking similarities among these methods using Spearman's relationship figure. We describe the algorithms of these six FMCDMs in the methods section. In a case study, we collected primary data from five experts who rated nine QoS factors of nine web services. We used modified online software for analysis. The results showed that S6 ranked first in all FMCDMs, except for FD and FP, where it was ranked 2nd and 8th, respectively. The highest association coefficient (Rs) was found between FT and FV ranking in S techniques (0.983), FV ranking in S and FV ranking in Q (0.883), and FT and FV ranking Q (0.833) when comparing the similarity measure of the FMCDMMs. This analysis helps decision-makers and researchers choose the most suitable methods for integrated FMCDMs studies and real-world problem-solving.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Control system optimisation of biodiesel-based gas turbine for ship propulsion 优化用于船舶推进的生物柴油燃气轮机控制系统

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1992-2002

A. Machmudah, E. A. Bakar, Ranjendran R, Wibowo Harso Nugroho, M. I. Solihin, Abdul Ghofur

{"title":"Control system optimisation of biodiesel-based gas turbine for ship propulsion","authors":"A. Machmudah, E. A. Bakar, Ranjendran R, Wibowo Harso Nugroho, M. I. Solihin, Abdul Ghofur","doi":"10.11591/ijai.v13.i2.pp1992-2002","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1992-2002","url":null,"abstract":"Reducing a gas emission of shipping transportations become a main goal of international maritime organization to achieve a clean energy. One of best scenarios to achieve this goal is to shift a fossil fuel to a renewable energy-based fuel of a ship propulsion. This paper studies an optimization of a control system of the renewable-based small gas turbine engine for the ship propulsion. Proposed control system consists of a proportional-integral with engine performance limiters to avoid an engine damage. Proportional-integral gains are tuned by a whale optimization algorithm. A gain scheduling analysis of a step response is performed to obtain a searching area of tuning parameters and values of constant gains. In this step, the gains are modeled as function of plant variables. After the searching area is obtained, the proportional-integral gains are optimized using the whale optimization algorithm while the additional gains are set as constant values. Using this scenario, stable and optimal gains have been successfully achieved. Results show that the proposed method has better performance than that of the previous methods, i.e. gain scheduling and gain scheduling optimized by the whale optimization algorithm. The proposed method has lowest fitness value and does not have an overshoot problem.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving job matching with deep learning-based hyper-personalization 利用基于深度学习的超个性化改进工作匹配

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1711-1722

Qusai Q. Abuein, M. Shatnawi, Nour Alqudah

{"title":"Improving job matching with deep learning-based hyper-personalization","authors":"Qusai Q. Abuein, M. Shatnawi, Nour Alqudah","doi":"10.11591/ijai.v13.i2.pp1711-1722","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1711-1722","url":null,"abstract":"This study introduces a novel approach to streamline the recruitment process, benefiting both employers and job seekers. It leverages real-time personality-based classification to match candidates with the most suitable roles in a scalable and precise manner. This is achieved through machine learning-driven hyper-personalization, employing deep learning models to create a predictive language model. The study encompasses two key tasks: binary classification, distinguishing sentences containing soft skills (1) from those that do not (0), and multi-class classification, categorizing positive sentences into five classes based on Big Five personality traits. The research involved a series of experiments. Initially, multiple machine learning algorithms were employed to establish baseline models. Subsequently, the study investigated the impact of deep learning versus these baseline models. The results demonstrated an accuracy of 0.79% and 0.68% for binary classification tasks, and 0.79% and 0.60% for multi-class classification tasks, using Support Vector Machines in the machine learning task, and Bidirectional Long Short-Term Memory in the deep learning task, respectively. This approach showcases promise in revolutionizing the job matching process, offering a more efficient and accurate means of connecting individuals with their ideal employment opportunities based on their unique soft skills and personality traits.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of genetic algorithm in network-on-chip based architecture 评估基于芯片网络架构的遗传算法

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1479-1488

Doraisamy Radha, Minal Moharir

{"title":"Evaluation of genetic algorithm in network-on-chip based architecture","authors":"Doraisamy Radha, Minal Moharir","doi":"10.11591/ijai.v13.i2.pp1479-1488","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1479-1488","url":null,"abstract":"An increase in the number of cores gives a significant bounce in performance than an improvement in any of the factors or hardware. Many core systems use network-on-chip (NoC) for efficient communications among the cores in the system. However, the problem with NoC-based communication is that it significantly consumes a large amount of power and energy because the number of routers increases with the increase in the number of cores in the system. Power consumed by such components leads to degradation of the performance. The placement of cores in the topology is non-deterministic polynomial-time hardness (NP-Hard) problem. The optimal placement of cores in NoC is essential as it minimizes latency and communication costs. Thus, the NP-Hard problem of placing cores is solved using genetic algorithm (GA) based quadtree topology. The proposed work shows the analysis of GA-based quadtree topology, which outperforms other topologies in most aspects. The performance evaluation of GA-based quadtree topology is based on latency, throughput, power, area, bisection bandwidth, and diameter. Comparing these parameters with other topologies shows the prominence of the quadtree topology. The evaluation is performed in the Booksim simulator, and the experimental results revealed that the proposed GA-based quad tree-based topology is efficient for NoC-based communications.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Congestion and throughput optimization protocol for providing better quality of service and experience 拥塞和吞吐量优化协议，提供更好的服务质量和体验

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2364-2373

Sathya Vijaykumar, Shiva Prakash Thyagaraj

{"title":"Congestion and throughput optimization protocol for providing better quality of service and experience","authors":"Sathya Vijaykumar, Shiva Prakash Thyagaraj","doi":"10.11591/ijai.v13.i2.pp2364-2373","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2364-2373","url":null,"abstract":"Multimedia traffic in Internet of Things applications is generated for various purposes and encompasses a wide range of multimedia data, including video streams, audio files, images, and sensor data. Network providers employ various strategies to handle multimedia traffic in IoT applications efficiently. But most of these methods have not considered optimizing the RTSP (Real-Time Streaming Protocol), RTP (Real-time Transport Protocol), and RTCP (Real-Time Control Protocol) to improve the throughput and QoS of the IoT applications. Hence, in this Congestion and Throughput Optimization Protocol (CTOP) work, we present a model which optimizes the RTSP, RTP, and RTCP protocol to improve the throughput and QoS. The CTOP model outperforms the Big Packet Protocol model in terms of average throughput, multimedia loss, delay, and energy consumption for both less and high-traffic scenarios. For less-level of traffic and high level of traffic, the CTOP model achieves a better average throughput, and average multimedia delay, reducing the average multimedia loss and average energy consumption in comparison to the existing BBP model. These results highlight the improved performance and efficiency of the CTOP model compared to the BBP model.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141234580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sentiment analysis of student feedback using attention-based RNN and transformer embedding 利用基于注意力的 RNN 和变换器嵌入对学生反馈进行情感分析

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2173-2184

Imad Zyout, Mo’ath Zyout

{"title":"Sentiment analysis of student feedback using attention-based RNN and transformer embedding","authors":"Imad Zyout, Mo’ath Zyout","doi":"10.11591/ijai.v13.i2.pp2173-2184","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2173-2184","url":null,"abstract":"Sentiment analysis systems aim to assess people’s opinions across various domains by collecting and categorizing feedback and reviews. In our study, researchers put forward a sentiment analysis system that leverages three distinct embedding techniques: automatic, global vectors (GloVe) for word representation, and bidirectional encoder representations from transformers (BERT). This system features an attention layer, with the best model chosen through rigorous comparisons. In developing the sentiment analysis model, we employed a hybrid dataset comprising students’ feedback and comments. This dataset comprises 3,820 comments, including 2,773 from formal evaluations and 1,047 generated by ChatGPT and prompting engineering. Our main motivation for integrating generative AI was to balance both positive and negative comments. We also explored recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional long short-term memory (Bi-LSTM), with and without pre-trained GloVe embedding. These techniques produced F-scores ranging from 67% to 69%. On the other hand, the sentiment model based on BERT, particularly its KERAS implementation, achieved higher F-scores ranging from 83% to 87%. The Bi-LSTM architecture outperformed other models and the inclusion of an attention layer further enhanced the performance, resulting in F-scores of 89% and 88% from the Bi-LSTM-BERT sentiment models, respectively.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence for choosing an agile method 选择敏捷方法的人工智能

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1557-1566

S. Merzouk, S. Bouhsissin, Touria Hamim, N. Sael, A. Marzak

引用次数: 0

Semi-supervised spectral clustering using shared nearest neighbour for data with different shape and density 利用共享近邻对不同形状和密度的数据进行半监督光谱聚类

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2283-2290

Yousheng Gao, Raihah Aminuddin, Raseeda Hamzah, Li Ang, Siti Khatijah Nor Abdul Rahim

引用次数: 0

Python scikit-fuzzy: developing a fuzzy expert system for diabetes diagnosis Python scikit-fuzzy：开发用于糖尿病诊断的模糊专家系统

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1398-1407

Tajul Rosli Razak, Ahmad Zia Ul-Saufie, Mohamad Hanis Yusoff, Mohammad Hafiz Ismail, Shukor Sanim Mohd Fauzi, N. A. Mohd Zaki

{"title":"Python scikit-fuzzy: developing a fuzzy expert system for diabetes diagnosis","authors":"Tajul Rosli Razak, Ahmad Zia Ul-Saufie, Mohamad Hanis Yusoff, Mohammad Hafiz Ismail, Shukor Sanim Mohd Fauzi, N. A. Mohd Zaki","doi":"10.11591/ijai.v13.i2.pp1398-1407","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1398-1407","url":null,"abstract":"Nowadays, improvements in diabetes detection that provide patients with vital information are needed. This is due to the fact that Diabetes mellitus has generated a worldwide epidemic that costs society and people. Also, patients tend to misread symptoms, and clinicians who collect insufficient data may produce erroneous outcomes. Therefore, this study aims to demonstrate that a programme that integrates expert advice such as decisions, recommendations, or solutions is an excellent method for reducing the incidence of diabetes. Specifically, this study intends to implement a fuzzy expert system that can detect and report the early stages of diabetes as a viable approach. Furthermore, since this programme is available to everyone, people may easily self-diagnose themselves if they have a blood glucose monitoring device. However, developing the fuzzy expert system for real-world situations, such as diabetes patients, using any programming tools is not straightforward. Therefore, this study will provide a comprehensive approach to constructing a fuzzy expert system using the popular programming language Python.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141230571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A benchmark of health insurance fraud detection using machine learning techniques 使用机器学习技术检测医疗保险欺诈的基准

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1925-1934

Ossama Cherkaoui, H. Anoun, A. Maizate

{"title":"A benchmark of health insurance fraud detection using machine learning techniques","authors":"Ossama Cherkaoui, H. Anoun, A. Maizate","doi":"10.11591/ijai.v13.i2.pp1925-1934","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1925-1934","url":null,"abstract":"Health insurance fraud is a complex problem that also has a significant financial impact. Recently, with the availability of large volumes of data and the evolution of computing power, machine learning techniques have become the preferred method for fraud detection. However, the main difficulty facing researchers in this field is the lack of real data sets and the absence of reliable fraud labels. Most published studies use aggregated provider-level or simulated data to test fraud detection algorithms, which may not deliver accurate results. The present study aims to provide a more accurate assessment of fraud detection methods by using real detailed health insurance claims data to compare six of the most common supervised classification algorithms including neural networks and the use of two categorical feature preparation methods. The study was conducted under the guidance of insurance experts, who provided the fraud label inference rules and reviewed the results. A comprehensive description of the benchmarking process and an interpretation of the results are provided in this paper. The results show that supervised classification can be used effectively to detect health insurance fraud, improving detection accuracy by a factor of 4.2 (84% recall for a positive rate of 20%). ","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0