Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp451-458
M. Aparna, B. S. Rao
Alzheimer's disease is a degenerative brain illness, incurable and progressive. Globally for every two seconds, someone is affected by Alzheimer's disease. Alzheimer's disease in the elderly is difficult to diagnose due to the complexity of the brain structure. Its pixel intensity is similar and systematic distinction is necessary. Deep learning has inspired a lot of interest in recent years in tackling challenges in a variety of fields, including medical imaging. One of the drawbacks of deep learning approach is the inability to detect changes in functional connectivity in MCI (mild cognitive impairment) patients' functional brain networks. In this paper, we utilize deep features extracted from two pre-trained deep learning models to tackle this issue. The proposed models DenseNet121 and MobileNetV2 is used to perform the task of Alzheimer's disease multi-class classification. In this method, initially we increased 70 % of dataset and generated images by using CycleGAN (generative adversarial networks). We achieved 98.82% of accuracy with proposed models. It gives best results compared to existing models.
{"title":"A novel automated deep learning approach for Alzheimer's disease classification","authors":"M. Aparna, B. S. Rao","doi":"10.11591/ijai.v12.i1.pp451-458","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp451-458","url":null,"abstract":"Alzheimer's disease is a degenerative brain illness, incurable and progressive. Globally for every two seconds, someone is affected by Alzheimer's disease. Alzheimer's disease in the elderly is difficult to diagnose due to the complexity of the brain structure. Its pixel intensity is similar and systematic distinction is necessary. Deep learning has inspired a lot of interest in recent years in tackling challenges in a variety of fields, including medical imaging. One of the drawbacks of deep learning approach is the inability to detect changes in functional connectivity in MCI (mild cognitive impairment) patients' functional brain networks. In this paper, we utilize deep features extracted from two pre-trained deep learning models to tackle this issue. The proposed models DenseNet121 and MobileNetV2 is used to perform the task of Alzheimer's disease multi-class classification. In this method, initially we increased 70 % of dataset and generated images by using CycleGAN (generative adversarial networks). We achieved 98.82% of accuracy with proposed models. It gives best results compared to existing models.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65348686","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}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp48-56
N. A. Bakar, I. S. Chairul, S. Ghani, M. S. Ahmad Khiar, M. Z. Che Wanik
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp57-65
Swapna Subudhiray, H. Palo, Niva Das
In this paper, an experiment has been carried out based on a simple k-nearest neighbor (kNN) classifier to investigate the capabilities of three extracted facial features for the better recognition of facial emotions. The feature extraction techniques used are histogram of oriented gradient (HOG), Gabor, and local binary pattern (LBP). A comparison has been made using performance indices such as average recognition accuracy, overall recognition accuracy, precision, recall, kappa coefficient, and computation time. Two databases, i.e., Cohn-Kanade (CK+) and Japanese female facial expression (JAFFE) have been used here. Different training to testing data division ratios is explored to find out the best one from the performance point of view of the three extracted features, Gabor produced 94.8%, which is the best among all in terms of average accuracy though the computational time required is the highest. LBP showed 88.2% average accuracy with a computational time less than that of Gabor while HOG showed minimum average accuracy of 55.2% with the lowest computation time.
{"title":"K-nearest neighbor based facial emotion recognition using effective features","authors":"Swapna Subudhiray, H. Palo, Niva Das","doi":"10.11591/ijai.v12.i1.pp57-65","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp57-65","url":null,"abstract":"In this paper, an experiment has been carried out based on a simple k-nearest neighbor (kNN) classifier to investigate the capabilities of three extracted facial features for the better recognition of facial emotions. The feature extraction techniques used are histogram of oriented gradient (HOG), Gabor, and local binary pattern (LBP). A comparison has been made using performance indices such as average recognition accuracy, overall recognition accuracy, precision, recall, kappa coefficient, and computation time. Two databases, i.e., Cohn-Kanade (CK+) and Japanese female facial expression (JAFFE) have been used here. Different training to testing data division ratios is explored to find out the best one from the performance point of view of the three extracted features, Gabor produced 94.8%, which is the best among all in terms of average accuracy though the computational time required is the highest. LBP showed 88.2% average accuracy with a computational time less than that of Gabor while HOG showed minimum average accuracy of 55.2% with the lowest computation time.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47240623","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}
Many educators have used the benefit offer by intelligent tutoring system. To become more personalizing and effective tutoring system, student characteristics need to be considered. One of important student characteristic is motivation. Therefore, in this study a motivation assessment model based on self-efficacy theory was proposed. Refer to the theory, effort, choice of activities, performance and persistence were discussed as motivation attributes. Further, time spend, difficulty level, number of correct answers and number of questions skipped are the parameters was defined for each attribute. The model was designed by taking the advantages of Mamdani inference system as fuzzy logic technique to predict students’ motivation level. The model able to inmates like a human tutor does in the traditional classroom to understand students’ motivation level.
{"title":"Motivation assessment model for intelligent tutoring system based on mamdani inference system","authors":"Rajermani Thinakaran, Suriayati Chupra, Malathy Batumalay","doi":"10.11591/ijai.v12.i1.pp189-200","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp189-200","url":null,"abstract":"Many educators have used the benefit offer by intelligent tutoring system. To become more personalizing and effective tutoring system, student characteristics need to be considered. One of important student characteristic is motivation. Therefore, in this study a motivation assessment model based on self-efficacy theory was proposed. Refer to the theory, effort, choice of activities, performance and persistence were discussed as motivation attributes. Further, time spend, difficulty level, number of correct answers and number of questions skipped are the parameters was defined for each attribute. The model was designed by taking the advantages of Mamdani inference system as fuzzy logic technique to predict students’ motivation level. The model able to inmates like a human tutor does in the traditional classroom to understand students’ motivation level.","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43681552","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}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp305-314
Naga Sunil Kumar Gandikota, Mohd Hilmi Hasan, Jafreezal Jaafar
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 & 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.
{"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":null,"pages":null},"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}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp443-450
Shafaf Ibrahim, Khyrina Airin Fariza Abu Samah, Raseeda Hamzah, Nurul Amira Mohd Ali, Raihah Aminuddin
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-03-01DOI: 10.11591/ijai.v12.i1.pp87-95
Deepa Deepa, Jyothi Kupparu
Autonomous vehicle is one the prominent area of research in computer vision. In today’s AI world, the concept of autonomous vehicles has become popular largely to avoid accidents due to negligence of driver. Perceiving the depth of the surrounding region accurately is a challenging task in autonomous vehicles. Sensors like light detection and ranging can be used for depth estimation but these sensors are expensive. Hence stereo matching is an alternate solution to estimate the depth. The main difficulties observed in stereo matching is to minimize mismatches in the ill-posed regions, like occluded, texture less and discontinuous regions. This paper presents an efficient deep stereo matching technique for estimating disparity map from stereo images in ill-posed regions. The images from Middlebury stereo data set are used to assess the efficacy of the model proposed. The experimental outcome dipicts that the proposed model generates reliable results in the occluded, texture less and discontinuous regions as compared to the existing techniques.
{"title":"A deep learning based stereo matching model for autonomous vehicle","authors":"Deepa Deepa, Jyothi Kupparu","doi":"10.11591/ijai.v12.i1.pp87-95","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp87-95","url":null,"abstract":"<p><span lang=\"EN-US\">Autonomous vehicle is one the prominent area of research in computer vision. In today’s AI world, the concept of autonomous vehicles has become popular largely to avoid accidents due to negligence of driver. Perceiving the depth of the surrounding region accurately is a challenging task in autonomous vehicles. Sensors like light detection and ranging can be used for depth estimation but these sensors are expensive. Hence stereo matching is an alternate solution to estimate the depth. The main difficulties observed in stereo matching is to minimize mismatches in the ill-posed regions, like occluded, texture less and discontinuous regions. This paper presents an efficient deep stereo matching technique for estimating disparity map from stereo images in ill-posed regions. The images from Middlebury stereo data set are used to assess the efficacy of the model proposed. The experimental outcome dipicts that the proposed model generates reliable results in the occluded, texture less and discontinuous regions as compared to the existing techniques.</span></p>","PeriodicalId":52221,"journal":{"name":"IAES International Journal of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135907381","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}
Pub Date : 2023-01-01DOI: 10.11591/ijai.v12.i3.pp1149-1157
Sreya John, Arul Leena Rose Peter Joseph
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-01-01DOI: 10.11591/ijai.v12.i3.pp1224-1237
Ahmed Jamal Ahmed, Ali Hashim Abbas, Sami Abduljabbar Rashid
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-01-01DOI: 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
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.
{"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":null,"pages":null},"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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：