Pub Date : 2023-01-01DOI: 10.11591/ijai.v12.i4.pp1793-1802
Khyrina Airin Fariza Abu Samah, Nur Maisarah Nor Azharludin, L. Riza, M.N.H. Hasrol Jono, N. A. Moketar
Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.
{"title":"Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals","authors":"Khyrina Airin Fariza Abu Samah, Nur Maisarah Nor Azharludin, L. Riza, M.N.H. Hasrol Jono, N. A. Moketar","doi":"10.11591/ijai.v12.i4.pp1793-1802","DOIUrl":"https://doi.org/10.11591/ijai.v12.i4.pp1793-1802","url":null,"abstract":"Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.","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":"65354628","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.i4.pp1892-1900
Mohamed Ikermane, A. E. Mouatasim
Dyslexia is a neurodevelopmental impairment that causes difficulties in reading and can have significant academic, social, and economic impacts. In Morocco, Dyslexia accounts for 37% of children's school failures. Early detection of dyslexia is crucial to help children reach their academic potential and prevent low self-esteem. To address this issue, a dyslexia screening tool using webcam-based eye tracking was developed for the Arabic language. The tool was tested on a dataset of 61 students from three primary schools in southern Morocco, and the results showed that using Arabic dyslexic-friendly typefaces improved reading performance, particularly for those with low reading performance. Deep clustering was used to reduce the dimensionality of the dataset, and the subjects were gathered using unsupervised k-means based on AutoEncoder output. The three clusters produced showed a significant difference in many dyslexia traits, such as the number and duration of fixations, as well as the saccade period. These findings suggest that webcam-based eye-tracking techniques have the potential to be used as an initial dyslexia diagnosis tool to assess if a child exhibits some of the typical symptoms of dyslexia and whether they should seek a professional full dyslexia diagnosis.
{"title":"Dyslexia deep clustering using webcam-based eye tracking","authors":"Mohamed Ikermane, A. E. Mouatasim","doi":"10.11591/ijai.v12.i4.pp1892-1900","DOIUrl":"https://doi.org/10.11591/ijai.v12.i4.pp1892-1900","url":null,"abstract":"Dyslexia is a neurodevelopmental impairment that causes difficulties in reading and can have significant academic, social, and economic impacts. In Morocco, Dyslexia accounts for 37% of children's school failures. Early detection of dyslexia is crucial to help children reach their academic potential and prevent low self-esteem. To address this issue, a dyslexia screening tool using webcam-based eye tracking was developed for the Arabic language. The tool was tested on a dataset of 61 students from three primary schools in southern Morocco, and the results showed that using Arabic dyslexic-friendly typefaces improved reading performance, particularly for those with low reading performance. Deep clustering was used to reduce the dimensionality of the dataset, and the subjects were gathered using unsupervised k-means based on AutoEncoder output. The three clusters produced showed a significant difference in many dyslexia traits, such as the number and duration of fixations, as well as the saccade period. These findings suggest that webcam-based eye-tracking techniques have the potential to be used as an initial dyslexia diagnosis tool to assess if a child exhibits some of the typical symptoms of dyslexia and whether they should seek a professional full dyslexia diagnosis.","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":"65355368","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}
Speaker recognition to recognize multiethnic speakers is an interesting research topic. Various studies involving many ethnicities require the right approach to achieve optimal model performance. The deep learning approach has been used in speaker recognition research involving many classes to achieve high accuracy results with promising results. However, multi-class and imbalanced datasets are still obstacles encountered in various studies using the deep learning method which cause overfitting and decreased accuracy. Data augmentation is an approach model used in overcoming the problem of small amounts of data and multiclass problems. This approach can improve the quality of research data according to the method applied. This study proposes a data augmentation method using pitch shifting with a deep neural network called pitch shifting data augmentation deep neural network (PSDA-DNN) to identify multiethnic Indonesian speakers. The results of the research that has been done prove that the PSDA-DNN approach is the best method in multi-ethnic speaker recognition where the accuracy reaches 99.27% and the precision, recall, F1 score is 97.60%.
{"title":"Improving Indonesian multietnics speaker recognition using pitch shifting data augmentation","authors":"Kristiawan Nugroho, Isworo Nugroho, De Rosal Ignatius Moses Setiadi, Omar Farooq","doi":"10.11591/ijai.v12.i4.pp1901-1908","DOIUrl":"https://doi.org/10.11591/ijai.v12.i4.pp1901-1908","url":null,"abstract":"Speaker recognition to recognize multiethnic speakers is an interesting research topic. Various studies involving many ethnicities require the right approach to achieve optimal model performance. The deep learning approach has been used in speaker recognition research involving many classes to achieve high accuracy results with promising results. However, multi-class and imbalanced datasets are still obstacles encountered in various studies using the deep learning method which cause overfitting and decreased accuracy. Data augmentation is an approach model used in overcoming the problem of small amounts of data and multiclass problems. This approach can improve the quality of research data according to the method applied. This study proposes a data augmentation method using pitch shifting with a deep neural network called pitch shifting data augmentation deep neural network (PSDA-DNN) to identify multiethnic Indonesian speakers. The results of the research that has been done prove that the PSDA-DNN approach is the best method in multi-ethnic speaker recognition where the accuracy reaches 99.27% and the precision, recall, F1 score is 97.60%.","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":"65355388","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.i1.pp162-170
Soumia Bensaadi, A. Louchene
Agriculture is a crucial element to build a strong economy, not only because of its importance in providing food, but also as a source of raw materials for industry as well as source of energy. Different diseases affect plants, which leads to decrease in productivity. In recent years, developments in computing technology and machine-learning algorithms (such as deep neural networks) in the field of agriculture have played a great role to face this problem by building early detection tools. In this paper, we propose an automatic plant disease classification based on a low complexity convolutional neural network (CNN) architecture, which leads to faster on-line classification. For the training process, we used more than one 57.000 tomato leaf images representing nine classes, taken under natural environment, and considered during training without background subtraction. The designed model achieves 97.04% classification accuracy and less than 0.2 error, which shows a high accuracy in distinguishing a disease from another.
{"title":"Low-cost convolutional neural network for tomato plant diseases classifiation","authors":"Soumia Bensaadi, A. Louchene","doi":"10.11591/ijai.v12.i1.pp162-170","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp162-170","url":null,"abstract":"Agriculture is a crucial element to build a strong economy, not only because of its importance in providing food, but also as a source of raw materials for industry as well as source of energy. Different diseases affect plants, which leads to decrease in productivity. In recent years, developments in computing technology and machine-learning algorithms (such as deep neural networks) in the field of agriculture have played a great role to face this problem by building early detection tools. In this paper, we propose an automatic plant disease classification based on a low complexity convolutional neural network (CNN) architecture, which leads to faster on-line classification. For the training process, we used more than one 57.000 tomato leaf images representing nine classes, taken under natural environment, and considered during training without background subtraction. The designed model achieves 97.04% classification accuracy and less than 0.2 error, which shows a high accuracy in distinguishing a disease from another.","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":"65347417","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.i1.pp251-261
Ferdiansyah Ferdiansyah, S. H. Othman, Raja Zahilah Md Radzi, D. Stiawan, T. Sutikno
Cryptocurrency is a virtual or digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency, decentralization, and conservation. Cryptocurrency prices have a high level of fluctuation; thus, tools are needed to monitor and predict them. RNN is a deep learning model that is capable of strongly predicting data time series. Some types of Recurrent Nureal Network layers, such as Long Short Term Memory, have been used in previous studies to prediction common used currency. In this study, we used the Gate Recurrent Unit and Bidirectional–LSTM hybrid model to predict cryptocurrency prices to improve the accuracy of previously proposed prediction LSTM Model to predict the Bitcoin, Using four cryptocurrencies (Bitcoin, Ehtereum, Ripple, and Binance), we obtained very good results with RMSE after normalization the results get closer to 0 and with MAPE values all below <10%.
{"title":"Hybrid gated recurrent unit bidirectional-long short-term memory model to improve cryptocurrency prediction accuracy","authors":"Ferdiansyah Ferdiansyah, S. H. Othman, Raja Zahilah Md Radzi, D. Stiawan, T. Sutikno","doi":"10.11591/ijai.v12.i1.pp251-261","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp251-261","url":null,"abstract":"Cryptocurrency is a virtual or digital currency used in financial systems that utilizes blockchain technology and cryptographic functions to gain transparency, decentralization, and conservation. Cryptocurrency prices have a high level of fluctuation; thus, tools are needed to monitor and predict them. RNN is a deep learning model that is capable of strongly predicting data time series. Some types of Recurrent Nureal Network layers, such as Long Short Term Memory, have been used in previous studies to prediction common used currency. In this study, we used the Gate Recurrent Unit and Bidirectional–LSTM hybrid model to predict cryptocurrency prices to improve the accuracy of previously proposed prediction LSTM Model to predict the Bitcoin, Using four cryptocurrencies (Bitcoin, Ehtereum, Ripple, and Binance), we obtained very good results with RMSE after normalization the results get closer to 0 and with MAPE values all below <10%.","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":"65347741","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.i1.pp66-78
Ziad El Khatib, A. B. Mnaouer, S. Moussa, Omar Mashaal, N. A. Ismail, Mohd Azman Bin Abas, Fuad Abdulgaleel
A neural network-based parking system with real-time license plate detection and vacant space detection using hyper parameter optimization is presented. When number of epochs increased from 30, 50 to 80 and learning rate tuned to 0.001, the validation loss improved to 0.017 and training object loss improved to 0.040. The model mean average precision mAP_0.5 is improved to 0.988 and the precision is improved to 99%. The proposed neural network-based parking system also uses a regularization technique for effective predictive modeling. The proposed modified lasso ridge elastic (LRE) regularization technique provides a 5.21 root mean square error (RMSE) and an R-square of 0.71 with a 4.22 mean absolute error (MAE) indicative of higher accuracy performance compared to other regularization regression models. The advantage of the proposed modified LRE is that it enables effective regularization via modified penalty with the feature selection characteristics of both lasso and ridge.
{"title":"Neural network-based parking system object detection and predictive modeling","authors":"Ziad El Khatib, A. B. Mnaouer, S. Moussa, Omar Mashaal, N. A. Ismail, Mohd Azman Bin Abas, Fuad Abdulgaleel","doi":"10.11591/ijai.v12.i1.pp66-78","DOIUrl":"https://doi.org/10.11591/ijai.v12.i1.pp66-78","url":null,"abstract":"A neural network-based parking system with real-time license plate detection and vacant space detection using hyper parameter optimization is presented. When number of epochs increased from 30, 50 to 80 and learning rate tuned to 0.001, the validation loss improved to 0.017 and training object loss improved to 0.040. The model mean average precision mAP_0.5 is improved to 0.988 and the precision is improved to 99%. The proposed neural network-based parking system also uses a regularization technique for effective predictive modeling. The proposed modified lasso ridge elastic (LRE) regularization technique provides a 5.21 root mean square error (RMSE) and an R-square of 0.71 with a 4.22 mean absolute error (MAE) indicative of higher accuracy performance compared to other regularization regression models. The advantage of the proposed modified LRE is that it enables effective regularization via modified penalty with the feature selection characteristics of both lasso and ridge.","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":"65348894","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.i2.pp851-860
Meilinda Fitriani Nur Maghfiroh, A. A. N. Perwira Redi, Janice Ong, M. Fikri
A novel metaheuristic optimization algorithm based on cuckoo search algorithm (CSA) is presented to solve the construction site layout planning problem (CSLP). CSLP is a complex optimization problem with various applications, such as plant layout, construction site layout, and computer chip layout. Many researchers have investigated the CSLP by applying many algorithms in an exact or heuristic approach. Although both methods yield a promising result, technically, nature-inspired algorithms demonstrate high achievement in successful percentage. In the last two decades, researchers have been developing a new nature-inspired algorithm for solving different types of optimization problems. The CSA has gained popularity in resolving large and complex issues with promising results compared with other nature-inspired algorithms. However, for solving CSLP, the algorithm based on CSA is still minor. Thus, this study proposed CSA with additional modification in the algorithm mechanism, where the algorithm shows a promising result and can solve CSLP cases.
{"title":"Cuckoo search algorithm for construction site layout planning","authors":"Meilinda Fitriani Nur Maghfiroh, A. A. N. Perwira Redi, Janice Ong, M. Fikri","doi":"10.11591/ijai.v12.i2.pp851-860","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp851-860","url":null,"abstract":"A novel metaheuristic optimization algorithm based on cuckoo search algorithm (CSA) is presented to solve the construction site layout planning problem (CSLP). CSLP is a complex optimization problem with various applications, such as plant layout, construction site layout, and computer chip layout. Many researchers have investigated the CSLP by applying many algorithms in an exact or heuristic approach. Although both methods yield a promising result, technically, nature-inspired algorithms demonstrate high achievement in successful percentage. In the last two decades, researchers have been developing a new nature-inspired algorithm for solving different types of optimization problems. The CSA has gained popularity in resolving large and complex issues with promising results compared with other nature-inspired algorithms. However, for solving CSLP, the algorithm based on CSA is still minor. Thus, this study proposed CSA with additional modification in the algorithm mechanism, where the algorithm shows a promising result and can solve CSLP cases.","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":"65349613","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.i2.pp874-883
Rao Faizan Ali, Amgad Muneer, Ahmed Almaghthawi, Amal Alghamdi, Suliman Mohamed Fati, Ebrahim Abdulwasea Abdullah Ghaleb
Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an essential element is to clear the missing data and perform proper feature engineering to better understand them before applying them. The experimental results show that the random forest predictor has outperformed ridge regression, linear regression, and decision tree models among the four machine learning techniques implemented in this study. The performance of the proposed models has been evaluated using root mean square error (RMSE).
{"title":"BMSP-ML: big mart sales prediction using different machine learning techniques","authors":"Rao Faizan Ali, Amgad Muneer, Ahmed Almaghthawi, Amal Alghamdi, Suliman Mohamed Fati, Ebrahim Abdulwasea Abdullah Ghaleb","doi":"10.11591/ijai.v12.i2.pp874-883","DOIUrl":"https://doi.org/10.11591/ijai.v12.i2.pp874-883","url":null,"abstract":"Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an essential element is to clear the missing data and perform proper feature engineering to better understand them before applying them. The experimental results show that the random forest predictor has outperformed ridge regression, linear regression, and decision tree models among the four machine learning techniques implemented in this study. The performance of the proposed models has been evaluated using root mean square error (RMSE).","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":"65349851","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.pp1139-1148
L. Srinivasan, Reshma Verma, Mysore Dakshinamurthy Nandeesh
Diabetes can be mentioned as one of the most lethal and constant sicknesses that may cause an arise in the glucose levels. Design and development of performance efficient diagnosis tool is important and plays a vigorous role in initial prediction of disease and help medical experts to start with suitable treatment or medication. The insulin produced by pancreases in the subject’s body will be affected leading to several dysfunctionalities to various body organs such as kidney, heart eyes and nervous system with their normal functionalities. Hence, preliminary stage detection with proper care and medication could reduce the risk of these problems. In the area of medicine to discover patient’s data as well as to attain a predictive model or a set of rules, classification techniques have been continuously used. This study helped diagnose diabetes by selecting three important artificial intelligence techniques namely the optimal decision tree algorithm model, Type-2 fuzzy expert system and adaptive neuro fuzzy inference system which is modified. In the present research work, a hybrid model is proposed in order to improve the classification prediction and accuracy. The Pima Indian diabetes dataset from machine learning repository dataset was used to carry out validation and predication of the model accuracy.
{"title":"Early prediction of diabetes diagnosis using hybrid classification techniques","authors":"L. Srinivasan, Reshma Verma, Mysore Dakshinamurthy Nandeesh","doi":"10.11591/ijai.v12.i3.pp1139-1148","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp1139-1148","url":null,"abstract":"Diabetes can be mentioned as one of the most lethal and constant sicknesses that may cause an arise in the glucose levels. Design and development of performance efficient diagnosis tool is important and plays a vigorous role in initial prediction of disease and help medical experts to start with suitable treatment or medication. The insulin produced by pancreases in the subject’s body will be affected leading to several dysfunctionalities to various body organs such as kidney, heart eyes and nervous system with their normal functionalities. Hence, preliminary stage detection with proper care and medication could reduce the risk of these problems. In the area of medicine to discover patient’s data as well as to attain a predictive model or a set of rules, classification techniques have been continuously used. This study helped diagnose diabetes by selecting three important artificial intelligence techniques namely the optimal decision tree algorithm model, Type-2 fuzzy expert system and adaptive neuro fuzzy inference system which is modified. In the present research work, a hybrid model is proposed in order to improve the classification prediction and accuracy. The Pima Indian diabetes dataset from machine learning repository dataset was used to carry out validation and predication of the model accuracy.","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":"65350718","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.pp1260-1269
Mariam El jaadi, Touria Haidi, Doha Bouabdallaoui
The wind turbine’s output power is heavily affected by the arrangement of the wind turbine location. Wind farm planning endeavors to firstly maximize the farm’s output energy. Secondly, it seeks to minimize the effects of the wake phenomenon. This paper attempts to find the best possible location of a wind turbine inside a square farm using the particle swarm optimization (PSO) method whilst focusing on the three salient cases: the steadiness of wind direction and speed, the variability of the flow direction with a steady speed, and the variability of direction for three discrete wind speeds. The proposed algorithm generated results that will be contrasted to previous studies on the same topic with different metaheuristic methods such as a genetic algorithm. When compared to the optimum findings from prior research, the suggested approach has a reduced cost. It is developed by language C through MATLAB environment considering a square with the dimensions 2×2 kilometers.
{"title":"Particle swarm optimization for the optimal layout of wind turbines inside a wind farm","authors":"Mariam El jaadi, Touria Haidi, Doha Bouabdallaoui","doi":"10.11591/ijai.v12.i3.pp1260-1269","DOIUrl":"https://doi.org/10.11591/ijai.v12.i3.pp1260-1269","url":null,"abstract":"The wind turbine’s output power is heavily affected by the arrangement of the wind turbine location. Wind farm planning endeavors to firstly maximize the farm’s output energy. Secondly, it seeks to minimize the effects of the wake phenomenon. This paper attempts to find the best possible location of a wind turbine inside a square farm using the particle swarm optimization (PSO) method whilst focusing on the three salient cases: the steadiness of wind direction and speed, the variability of the flow direction with a steady speed, and the variability of direction for three discrete wind speeds. The proposed algorithm generated results that will be contrasted to previous studies on the same topic with different metaheuristic methods such as a genetic algorithm. When compared to the optimum findings from prior research, the suggested approach has a reduced cost. It is developed by language C through MATLAB environment considering a square with the dimensions 2×2 kilometers.","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":"65351349","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}
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