IAES International Journal of Artificial Intelligence最新文献_第9页

A novel automated deep learning approach for Alzheimer's disease classification 一种新的用于阿尔茨海默病分类的自动深度学习方法

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp451-458

M. Aparna, B. S. Rao

引用次数: 2

Improvement of transformer dissolved gas analysis interpretation using j48 decision tree model j48决策树模型对变压器溶解气体分析解释的改进

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp48-56

N. A. Bakar, I. S. Chairul, S. Ghani, M. S. Ahmad Khiar, M. Z. Che Wanik

{"title":"Improvement of transformer dissolved gas analysis interpretation using j48 decision tree model","authors":"N. A. Bakar, I. S. Chairul, S. Ghani, M. S. Ahmad Khiar, M. Z. Che Wanik","doi":"10.11591/ijai.v12.i1.pp48-56","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp48-56","url":null,"abstract":"Dissolved gas analysis (DGA) is widely accepted as an effective method to detect incipient faults within power transformers. Gases such as hydrogen, methane, acetylene, ethylene and ethane are normally utilized to identify the transformer fault conditions. Several techniques have been developed to interpret DGA results such as the key gas method, Doernenburg, Rogers, IEC ratio-based methods, Duval Triangles, and the latest Duval Pentagon methods. However, each of these approaches depends on the experts' shared knowledge and experience rather than quantitative scientific methods, therefore different diagnoses may be reported for the same oil sample. To overcome these shortcomings, this paper proposed the use of decision tree method to interpret the transformer health condition based on DGA results. The proposed decision tree model employed three main fault gases; methane, acetylene, ethylene as inputs, and classified the transformer into eight fault conditions. The J48 algorithm is used to train and developed the decision tree model. The performance of the proposed model is validated with the pre-known condition of transformers and compared with the Duval Triangle method. Results show that the proposed model delivers better precision and accuracy in predicting transformer fault conditions compared to DTM with 81% and 69% respectively.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65348766","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

K-nearest neighbor based facial emotion recognition using effective features 基于k近邻的有效特征面部情感识别

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp57-65

Swapna Subudhiray, H. Palo, Niva Das

引用次数: 2

Motivation assessment model for intelligent tutoring system based on mamdani inference system 基于mamdani推理系统的智能辅导系统动机评估模型

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp189-200

Rajermani Thinakaran, Suriayati Chupra, Malathy Batumalay

引用次数: 0

An adaptive metaheuristic approach for risk-budgeted portfolio optimization 风险预算投资组合优化的自适应元启发式方法

IAES International Journal of Artificial Intelligence Pub Date : 2023-03-01 DOI: 10.11591/ijai.v12.i1.pp305-314

Naga Sunil Kumar Gandikota, Mohd Hilmi Hasan, Jafreezal Jaafar

{"title":"An adaptive metaheuristic approach for risk-budgeted portfolio optimization","authors":"Naga Sunil Kumar Gandikota, Mohd Hilmi Hasan, Jafreezal Jaafar","doi":"10.11591/ijai.v12.i1.pp305-314","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp305-314","url":null,"abstract":"<div align=\"left\"><span lang=\"EN-US\">An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential &amp; arithmetic) together with the hall of fame via differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study.</span></div>","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":"136132081","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

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

Blockchain and machine learning in the internet of things: a review of smart healthcare 区块链和物联网中的机器学习:智能医疗回顾

IAES International Journal of Artificial Intelligence Pub Date : 2023-01-01 DOI: 10.11591/ijai.v12.i3.pp995-1006

Nwadher Suliman Al-Blihed, Nouf Fahad Al-Mufadi, Nouf Thyab Al-Harbi, Ibrahim Ahmed Al-Omari, Mohammed Abdullah Al-Hagery

{"title":"Blockchain and machine learning in the internet of things: a review of smart healthcare","authors":"Nwadher Suliman Al-Blihed, Nouf Fahad Al-Mufadi, Nouf Thyab Al-Harbi, Ibrahim Ahmed Al-Omari, Mohammed Abdullah Al-Hagery","doi":"10.11591/ijai.v12.i3.pp995-1006","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp995-1006","url":null,"abstract":"The healthcare sector has benefited from digital transformation and modern technology. As well is expected to rely even more on the internet of things (IoT) technologies in the near future. Due to the availability of portable medical devices, applications, and mobile health services, all of which have contributed to the development of innovative features for the delivery of healthcare services. With the large number of data issued from the IoT and the importance of using data to benefit from contained in diagnosing diseases, medical records, or monitoring. Furthermore, the expansion of emerging technologies such as robots and machine learning (ML) is supported by the ease with exchanged and shared medical information. Moreover, Blockchain technology enables the creation of secure records for storing medical data in a safe and timely manner. The paper reviews various IoT, Blockchain, and ML applications and systems in the smart healthcare sector to discover many challenges, consequently, it will be easy for researchers who have an interest in these fields to find today and future solutions. This, in turn, will help to enhance the technical services depending on the IoT in ML and Blockchain in the smart healthcare field.","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":"65352723","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: 4