IAES International Journal of Artificial Intelligence最新文献_第7页

Intelligent system for Islamic prayer (salat) posture monitoring 用于伊斯兰礼拜(礼拜)姿势监控的智能系统

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp220-231

M. Rahman, Rayan Abbas Ahmed Alharazi, Muhammad Khairul Imban b Zainal Badri

{"title":"Intelligent system for Islamic prayer (salat) posture monitoring","authors":"M. Rahman, Rayan Abbas Ahmed Alharazi, Muhammad Khairul Imban b Zainal Badri","doi":"10.11591/ijai.v12.i1.pp220-231","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp220-231","url":null,"abstract":"This paper introduced an Intelligent Salat Monitoring and Training System based on machine vision and image processing. In Islam, prayer (i.e. salat) is the second pillar of Islam. It is the most important and fundamental worshipping activity that believers have to perform five times a day. From gestures’ perspective, there are predefined human postures that must be performed in a precise manner. There are lots of materials on the internet and social media for training and correction purposes. However, some people do not perform these postures correctly due to being new to salat or even having learned prayers incorrectly. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an assistive intelligence framework that guides worshippers to evaluate the correctness of their prayer’s postures. Image comparison and pattern matching are used to study the system’s effectiveness by using several combining algorithms, such as Euclidean distance, template matching and grey-level correlation, to compare the images of the user and the database. The experiments’ results, both correct and incorrect salat performances, are shown via pictures and graph for each of the postures of salat.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47106289","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: 1

A convolutional neural network framework for classifying inappropriate online video contents 一种用于对不合适的在线视频内容进行分类的卷积神经网络框架

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp124-136

Tanatorn Tanantong, Patcharajak Yongwattana

{"title":"A convolutional neural network framework for classifying inappropriate online video contents","authors":"Tanatorn Tanantong, Patcharajak Yongwattana","doi":"10.11591/ijai.v12.i1.pp124-136","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp124-136","url":null,"abstract":"In the digital world, the Internet and online media especially video media are convenient and easy to access. It leads to problems of inappropriate content media consumption among children and youths. However, measures or methods to control the inappropriate content for children and young people are still a challenge for management. In this research, an automated model was developed and presented to classify the content on online video media using a deep learning technique namely convolution neural networks (CNN). For data collection and preparation, the researchers collected video clips from movies and television (TV) series from websites that distribute the clips online. It consists of different types of content: i) sexually inappropriate content; ii) violently inappropriate content; and iii) general content. The collected video clip data was then extracted into frames and then used for developing the automatically-content-classifying model with algorithm CNN, analyzing and comparing the result of CNN model performance. For enhancing the model performance, a transfer learning approach and different regularization techniques were adopted in order to find the most suitable method to create high-performance modeling to classify content in video clips, movies and TV series published online.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47814091","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: 3

Comparison of machine learning models for breast cancer diagnosis 癌症诊断的机器学习模型比较

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp415-421

Rania R. Kadhim, Mohammed Y. Kamil

{"title":"Comparison of machine learning models for breast cancer diagnosis","authors":"Rania R. Kadhim, Mohammed Y. Kamil","doi":"10.11591/ijai.v12.i1.pp415-421","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp415-421","url":null,"abstract":"Breast cancer is the most common cause of death among women worldwide. Breast cancer can be detected early, and the death rate can be reduced. Machine learning techniques are a hot topic for study and have proved influential in cancer prediction and early diagnosis. This study's objective is to predict and diagnose breast cancer using machine learning models and evaluate the most effective based on six criteria: specificity, sensitivity, precision, accuracy, F1-score and receiver operating characteristic curve. All work is done in the anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries, and pandas and matplotlib. This study used the Wisconsin diagnostic breast cancer dataset to test ten machine learning algorithms: decision tree, linear discriminant analysis, forests of randomized trees, gradient boosting, passive aggressive, logistic regression, naïve Bayes, nearest centroid, support vector machine, and perceptron. After collecting the findings, we performed a performance evaluation and compared these various classification techniques. Gradient boosting model outperformed all other algorithms, scoring 96.77% on the F1-score.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49615611","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: 6

Machine learning and artificial intelligence models development in rainfall-induced landslide prediction 降雨诱发滑坡预测中机器学习和人工智能模型的发展

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp262-270

Hastuadi Harsa, Anistia Malinda Hidayat, Adi Mulsandi, Bambang Suprihadi, Roni Kurniawan, Muhammad Najib Habibie, Thahir Daniel Hutapea, Yunus S. Swarinoto, Erwin Eka Syahputra Makmur, Welly Fitria, Rahayu Sapta Sri Sudewi, Alfan Sukmana Praja

{"title":"Machine learning and artificial intelligence models development in rainfall-induced landslide prediction","authors":"Hastuadi Harsa, Anistia Malinda Hidayat, Adi Mulsandi, Bambang Suprihadi, Roni Kurniawan, Muhammad Najib Habibie, Thahir Daniel Hutapea, Yunus S. Swarinoto, Erwin Eka Syahputra Makmur, Welly Fitria, Rahayu Sapta Sri Sudewi, Alfan Sukmana Praja","doi":"10.11591/ijai.v12.i1.pp262-270","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp262-270","url":null,"abstract":"<span lang=\"EN-US\">In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.</span>","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136132079","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

Impedance characteristic of the human arm during passive movements 人体手臂被动运动时的阻抗特性

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp34-40

M. Rahman, R. Ikeura

引用次数: 0

A new approach to achieve the users’ habitual opportunities on social media 一种实现用户在社交媒体上习惯性机会的新方法

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp41-47

Arif Ridho Lubis, M. K. Nasution, O. S. Sitompul, E. M. Zamzami

引用次数: 5

Substantial adaptive artificial bee colony algorithm implementation for glioblastoma detection 胶质母细胞瘤检测的实体自适应人工蜂群算法实现

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp443-450

Shafaf Ibrahim, Khyrina Airin Fariza Abu Samah, Raseeda Hamzah, Nurul Amira Mohd Ali, Raihah Aminuddin

{"title":"Substantial adaptive artificial bee colony algorithm implementation for glioblastoma detection","authors":"Shafaf Ibrahim, Khyrina Airin Fariza Abu Samah, Raseeda Hamzah, Nurul Amira Mohd Ali, Raihah Aminuddin","doi":"10.11591/ijai.v12.i1.pp443-450","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp443-450","url":null,"abstract":"Glioblastoma multiforme (GBM) is a high-grade brain tumor that is extremely dangerous and aggressive. Due to its rapid development rate, high-grade cancers require early detection and treatment, and early detection may possibly increase the chances of survival. The current practice of GBM detection is performed by a radiologist; due to the enormous number of cases, it is nevertheless tedious, intrusive, and error-prone. Thus, this study attempted a substantial adaptive artificial bee colony (a-ABC) algorithm implementation in providing a non-invasive approach for GBM detection. The basic statistical intensity-based analysis of minimum (minGL), maximum (maxGL), and mean (meanGL) of grey level data was employed to investigate the GBM's feature properties. The a-ABC's performance for adaptive GBM detection identification was evaluated using T1-weighted (T1), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and T1-contrast (T1C) which are four different magnetic resonance imaging (MRI) imaging sequences. Hundred and twenty MRI of GBM images were assessed in total, with 30 images per imaging sequence. The overall mean of GBM detection accuracy percentage was 93.67%, implying that the proposed a-ABC algorithm is capable of detecting GBM brain tumors. Other feature extraction strategies, on the other hand, may be added in the future to enhancee the performance of feature extraction. ","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43550189","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: 1

A deep learning based stereo matching model for autonomous vehicle 基于深度学习的自动驾驶汽车立体匹配模型

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp87-95

Deepa Deepa, Jyothi Kupparu

引用次数: 1

Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique 基于保亮度动态模糊直方图均衡化技术的叶片图像预处理

IAES International Journal of Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.11591/ijai.v12.i3.pp1149-1157

Sreya John, Arul Leena Rose Peter Joseph

{"title":"Preprocessing of leaf images using brightness preserving dynamic fuzzy histogram equalization technique","authors":"Sreya John, Arul Leena Rose Peter Joseph","doi":"10.11591/ijai.v12.i3.pp1149-1157","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp1149-1157","url":null,"abstract":"Agriculture serves as the backbone of many countries. It provides food and other essential materials as per our requirement. Various kinds of diseases are affecting the agricultural crops which in turn reduce the quantity and quality of the agricultural sector. This can also lead to the decrease in food production thereby affecting the economic growth and development. Even though the symptoms and other impacts of the diseases are outwardly visible, manual identification of diseases and rectification is a tedious and time-consuming process. Therefore, detecting the diseases using an automatic computer-based model will be an effective solution. Image processing methods in conjunction with machine learning algorithms provide greater assistance in the field of plant disease detection. In the proposed work, plant leaf images of 10 crops are collected as the dataset. The images after acquisition are preprocessed using brightness preserving dynamic fuzzy histogram equalization (BPDFHE), an advanced version of histogram equalization and Gaussian filtering. The results are calculated and compared using the parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM) and mean square error (MSE). This method performs more accurately than the existing preprocessing approaches.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65350962","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 level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network 基于改进蚁群优化的多级信任计算提高无线传感器网络的服务质量

IAES International Journal of Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.11591/ijai.v12.i3.pp1224-1237

Ahmed Jamal Ahmed, Ali Hashim Abbas, Sami Abduljabbar Rashid

{"title":"Multi level trust calculation with improved ant colony optimization for improving quality of service in wireless sensor network","authors":"Ahmed Jamal Ahmed, Ali Hashim Abbas, Sami Abduljabbar Rashid","doi":"10.11591/ijai.v12.i3.pp1224-1237","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp1224-1237","url":null,"abstract":"Wireless sensor network (WSN) is the most integral parts of current technology which are used for the real time applications. The major drawbacks in currect technologies are threads due to the creation of false trust values and data congestion. Maximum of the concept of WSNs primarily needs security and optimization. So, we are in the desire to develop a new model which is highly secured and localized. In this paper, we introduced a novel approach namely multi level trust calculation with improved ant colony optimization (MLT-IACO). This approach mainly sub-divided into two sections they are multi level trust calculation which is the combination three levels of trust such as direct trust, indirect trust and random repeat trust. Secondly, improved ant colony optimization technique is used to find the optimal path in the network. By transmitting the data in the optimal path, the congestion and delay of the network is reduced which leads to increase the efficiency. The outcome values are comparatively analyzed based the parameters such as packet delivery ratio, network throughput and average latency. While compared with the earlier research our MLT-IACO approach produce high packet delivery ratio and throughput as well as lower latency and routing overhead.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65351068","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: 13