IAES International Journal of Artificial Intelligence (IJ-AI)最新文献_第10页

Strategies for improving the quality of community detection based on modularity optimization 基于模块化优化的群落检测质量改进策略

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1794-1804

Tedy Setiadi, Mohd Ridzwan Yaakub, Azuraliza Abu Bakar

{"title":"Strategies for improving the quality of community detection based on modularity optimization","authors":"Tedy Setiadi, Mohd Ridzwan Yaakub, Azuraliza Abu Bakar","doi":"10.11591/ijai.v13.i2.pp1794-1804","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1794-1804","url":null,"abstract":"Community detection is a field of interest in social networks. Many new methods have emerged for community detection solution, however the modularity optimization method is the most prominent. Community detection based on modularity optimization (CDMO) has fundamental problems in the form of solution degeneration and resolution limits. From the two problems, the resolution limit is more concerned because it affects the resulting community's quality. During the last decade, many studies have attempted to address the problems, but so far they have been carried out partially, no one has thoroughly discussed efforts to improve the quality of CDMO. In this paper, we aim to investigate works in handling resolution limit and improving the quality of CDMO, along with their strengths and limitations. We derive six categories of strategies to improve the quality of CDMO, namely developing multi-resolution modularity, creating local modularity, creating modularity density, creating new metrics as an alternative to modularity, creating new quality metrics as a substitute for modularity, involving node attributes in determining community detection, and extending the single objective function into a multi-objective function. These strategies can be used as a guide in developing community detection methods. By considering network size, network type, and community distribution, we can choose the appropriate strategy in improving the quality of community detection.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":"13 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229699","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

Real-time indoor tracking for augmented reality using computer vision technique 利用计算机视觉技术实现增强现实的实时室内跟踪

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1845-1857

Ashraf Saad Shewail, Hala H. Zayed, Neven A. M. Elsayed

{"title":"Real-time indoor tracking for augmented reality using computer vision technique","authors":"Ashraf Saad Shewail, Hala H. Zayed, Neven A. M. Elsayed","doi":"10.11591/ijai.v13.i2.pp1845-1857","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1845-1857","url":null,"abstract":"In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229764","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

Combination of gray level co-occurrence matrix and artificial neural networks for classification of COVID-19 based on chest X-ray images 结合灰度共现矩阵和人工神经网络，基于胸部 X 光图像对 COVID-19 进行分类

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1625-1631

Bahtiar Imran, Lalu Delsi Samsumar, Ahmad Subki, Zaeniah Zaeniah, Salman Salman, Muhammad Rijal Alfian

引用次数: 0

Tuning the k value in k-nearest neighbors for malware detection 调整 k 近邻中的 k 值以检测恶意软件

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2275-2282

Mosleh M. Abualhaj, A. Abu-Shareha, Qusai Y. Shambour, S. Al-Khatib, Mohammad O. Hiari

{"title":"Tuning the k value in k-nearest neighbors for malware detection","authors":"Mosleh M. Abualhaj, A. Abu-Shareha, Qusai Y. Shambour, S. Al-Khatib, Mohammad O. Hiari","doi":"10.11591/ijai.v13.i2.pp2275-2282","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2275-2282","url":null,"abstract":"Malicious software, also referred to as malware, poses a serious threat to computer networks, user privacy, and user systems. Effective cybersecurity depends on the correct detection and classification of malware. In order to improve its effectiveness, the K-Nearest Neighbors (KNN) method is applied systematically in this study to the task of malware detection. The study investigates the effect of the number of neighbors (K) parameter on the KNN's performance. MalMem-2022 malware datasets and relevant evaluation criteria like accuracy, precision, recall, and F1-score will be used to assess the efficacy of the suggested technique. The experiments evaluate how parameter tuning affects the accuracy of malware detection by comparing the performance of various parameter setups. The study findings show that careful parameter adjustment considerably boosts the KNN method's malware detection capability. The research also highlights the potential of KNN with parameter adjustment as a useful tool for malware detection in real-world circumstances, allowing for prompt and precise identification of malware.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141235173","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

Evaluating the machine learning models based on natural language processing tasks 根据自然语言处理任务评估机器学习模型

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp1954-1968

Meeradevi Meeradevi, S. B. J., Swetha B. N.

{"title":"Evaluating the machine learning models based on natural language processing tasks","authors":"Meeradevi Meeradevi, S. B. J., Swetha B. N.","doi":"10.11591/ijai.v13.i2.pp1954-1968","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp1954-1968","url":null,"abstract":"In the realm of natural language processing (NLP), a diverse array of language models has emerged, catering to a wide spectrum of tasks, ranging from speaker recognition and auto-correction to sentiment analysis and stock prediction. The significance of language models in enabling the execution of these NLP tasks cannot be overstated. This study proposes an approach to enhance accuracy by leveraging a hybrid language model, combining the strengths of long short-term memory (LSTM) and gated recurrent unit (GRU). LSTM excels in preserving long-term dependencies in data, while GRU's simpler gating mechanism expedites the training process. The research endeavors to evaluate four variations of this hybrid model: LSTM, GRU, bidirectional long short-term memory (Bi-LSTM), and a combination of LSTM with GRU. These models are subjected to rigorous testing on two distinct datasets: one focused on IBM stock price prediction, and the other on Jigsaw toxic comment classification (sentiment analysis). This work represents a significant stride towards democratizing NLP capabilities, ensuring that even in resource-constrained settings, NLP models can exhibit improved performance. The anticipated implications of these findings span a wide spectrum of real-world applications and hold the potential to stimulate further research in the field of NLP. ","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":"28 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141232860","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

Multi-granularity tooth analysis via YOLO-based object detection models for effective tooth detection and classification 通过基于 YOLO 的物体检测模型进行多粒度牙齿分析，实现有效的牙齿检测和分类

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2081-2092

Samah W. G. AbuSalim, Nordin Zakaria, Aarish Maqsood, Abdul Saboor, Yew Kwang Hooi, Norehan Mokhtar, Said Jadid Abdulkadir

{"title":"Multi-granularity tooth analysis via YOLO-based object detection models for effective tooth detection and classification","authors":"Samah W. G. AbuSalim, Nordin Zakaria, Aarish Maqsood, Abdul Saboor, Yew Kwang Hooi, Norehan Mokhtar, Said Jadid Abdulkadir","doi":"10.11591/ijai.v13.i2.pp2081-2092","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2081-2092","url":null,"abstract":"Accurate detection and classification of teeth is the first step in dental disease diagnosis. However, the same class of tooth exhibits significant variations in surface appearance. Moreover, the complex geometrical structure poses challenges in learning discriminative features among the different tooth classes. Due to these complex features, tooth classification is one of the challenging research domains in deep learning. To address the aforementioned issues, the presented study proposes discriminative local feature extraction at different granular levels using YOLO models. However, this necessitates a granular intra-oral image dataset. To facilitate this requirement, a dataset at three granular levels (two, four, and seven teeth classes) is developed. YOLOv5, YOLOv6, and YOLOv7 models were trained using 2,790 images. The results indicate superior performance of YOLOv6 for two-class classification problems. The model generated a mean average precision (mAP) value of 94%. However, as the granularity level is increased, the performance of YOLO models decreases. For, four and seven-class classification problems, the highest mAP value of 87% and 79% was achieved by YOLOv5 respectively. The results indicate that different levels of granularity play an important role in tooth detection and classification. The YOLO’s performance gradually decreased as the granularity decreased especially at the finest granular level.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":"3 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229835","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

Feature selection techniques for microarray dataset: a review 微阵列数据集的特征选择技术：综述

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2395-2402

Avinash Nagaraja, S. Sinha, Shivamurthaiah Mallaiah

引用次数: 0

A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease 预测新发传染病传播的混合深度学习优化方法

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2036-2048

F. E. Nastiti, Shahrulniza Musa, Eiad Yafi

引用次数: 0

The performance analysis of hyper-heuristics algorithms over examination timetabling problems 超启发式算法在考试时间安排问题上的性能分析

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2155-2164

A. Muklason, Yusnardo Tendio, Helena Angelita Depari, Muhammad Arif Nuriman, Gusti Agung Premananda

{"title":"The performance analysis of hyper-heuristics algorithms over examination timetabling problems","authors":"A. Muklason, Yusnardo Tendio, Helena Angelita Depari, Muhammad Arif Nuriman, Gusti Agung Premananda","doi":"10.11591/ijai.v13.i2.pp2155-2164","DOIUrl":"https://doi.org/10.11591/ijai.v13.i2.pp2155-2164","url":null,"abstract":"In general, uncapacitated exam timetabling is conducted manually, which can be time-consuming. Many studies aim to automate and optimize uncapacitated exam timetabling. However, pinpointing the most efficient algorithm is challenging since most studies assert that their algorithms surpass previous ones. To identify the optimal algorithm, this research evaluates the performance of four algorithms: Hill climbing (HC), simulated annealing (SA), great deluge (GD), and tabu search (TS) in addressing the exam timetabling problem. The Kempe chain operator’s influence on optimization solutions is also examined. A simple random method is employed to select the low-level heuristic (LLH). The Carter (Toronto) dataset served as the test material, with each algorithm undergoing 200,000 iterations for comparison. The results indicate that the TS algorithm is superior, providing the best solution in 13 instances. The use of a tabu list enhanced the search process’s efficiency by preventing redundant modifications. The Kempe chain LLH exhibited a tendency towards achieving better solutions.","PeriodicalId":507934,"journal":{"name":"IAES International Journal of Artificial Intelligence (IJ-AI)","volume":"85 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141231003","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

Generative adversarial network-based phishing URL detection with variational autoencoder and transformer 利用变异自动编码器和变换器进行基于生成对抗网络的钓鱼 URL 检测

IAES International Journal of Artificial Intelligence (IJ-AI) Pub Date : 2024-06-01 DOI: 10.11591/ijai.v13.i2.pp2165-2172

Jishnu Kaitholikkal Sasi, Arthi Balakrishnan

引用次数: 0