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JournalISSN: 2089-4872

IAES International Journal of Artificial Intelligence 

Institute of Advanced Engineering and Science (IAES)
About: IAES International Journal of Artificial Intelligence is an academic journal published by Institute of Advanced Engineering and Science (IAES). The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 2089-4872. It is also open access. Over the lifetime, 682 publications have been published receiving 2453 citations. The journal is also known as: International Journal of Artificial Intelligence & IJAI.

Papers published on a yearly basis

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Journal ArticleDOI
TL;DR: Artificial Intelligence techniques and a Multi Linear Regression as the classical linear model for estimating the Water Quality Index (WQI) of Palla station of Yamuna river, India indicated that, the best model of both ANN and ANFIS proved high improvement in performance accuracy over MLR up to 10% in the verification phase.
Abstract: Water quality index is a measure of water quality at a certain location and over a period of time. High value indicates that the water is unsafe for drinking and inadequate in quality to meet the designated uses. Most of the classical models are unreliable producing unpromising forecasting results. This study presents Artificial Intelligence (AI) techniques and a Multi Linear Regression (MLR) as the classical linear model for estimating the Water Quality Index (WQI) of Palla station of Yamuna river, India. Full-scale data of the river were used in validating the models. Performance measures such as Mean Square Error (MSE), Root Mean Squared Error (RMSE) and Determination Coefficient (DC) were utilized in evaluating the accuracy and performance of the models. The obtained result depicted the superiority of AI models over the MLR model. The results also indicated that, the best model of both ANN and ANFIS proved high improvement in performance accuracy over MLR up to 10% in the verification phase. The difference between ANN and ANFIS accuracy is negligible due to a slight increment in performance accuracy indicating that both ANN and ANFIS could serve as reliable models for the estimation of WQI .

47 citations

Journal ArticleDOI
TL;DR: The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy.
Abstract: Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold crossvalidation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4%, 96.4%, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy

38 citations

Journal ArticleDOI
TL;DR: An ensemble deep learning method for TB detection using chest x-ray and Canny edge detected images introduces a new type of feature for the TB detection classifiers, thereby increasing the diversity of errors of the base classifiers.
Abstract: Tuberculosis (TB) is a disease caused by Mycobacterium Tuberculosis. Detection of TB at an early stage reduces mortality. Early stage TB is usually diagnosed using chest x-ray inspection. Since TB and lung cancer mimic each other, it is a challenge for the radiologist to avoid misdiagnosis. This paper presents an ensemble deep learning for TB detection using chest x-ray and Canny edge detected images. This method introduces a new type of feature for the TB detection classifiers, thereby increasing the diversity of errors of the base classifiers. The first set of features were extracted from the original x-ray images, while the second set of features were extracted from the edge detected image. To evaluate the proposed approach, two publicly available datasets were used. The results show that the proposed ensemble method produced the best accuracy of 89.77%, sensitivity of 90.91% and specificity of 88.64%. This indicates that using different types of features extracted from different types of images can improve the detection rate.

37 citations

Journal ArticleDOI
TL;DR: This work designs, implements and test a new IDS solution, baptized BotIDS, based on deep learning convolutional neural networks (CNN), and the obtained results are promising with 99.94% in validation accuracy, 0.58% in validate loss, and the prediction execution time is less than 0.34 ms.
Abstract: The massive network traffic data between connected devices in the internet of things have taken a big challenge to many traditional intrusion detection systems (IDS) to find probable security breaches. However, security attacks lean towards unpredictability. There are numerous difficulties to build up adaptable and powerful IDS for IoT in order to avoid false alerts and ensure a high recognition precision against attacks, especially with the rising of Botnet attacks. These attacks can even make harmless devices becoming zombies that send malicious traffic and disturb the network. In this paper, we propose a new IDS solution, baptized BotIDS, based on deep learning convolutional neural networks (CNN). The main interest of this work is to design, implement and test our IDS against some well-known Botnet attacks using a specific Bot-IoT dataset. Compared to other deep learning techniques, such as simple RNN, LSTM and GRU, the obtained results of our BotIDS are promising with 99.94% in validation accuracy, 0.58% in validation loss, and the prediction execution time is less than 0.34 ms.

36 citations

Journal ArticleDOI
TL;DR: A technique for automatically quantifying the damaged leaf area using k means clustering, which uses square Euclidian distances method for partition of leaf image is proposed, which shows that the proposed method is precise and reliable than visual evaluation performed by patahlogiest.
Abstract: Traditional method used for disease scoring scale to grade the plant diseases is mainly based on neckaed eye observation by agriculture expert or plant pathlogiest. In this method percentage scale was exclusively used to define different disease severities in an illustrated series of disease assessment keys for field crops.The assessment of plant leaf diseases using this aaproach which may be subjective, time consuming and cost effective.Also aacurate grading of leaf diseases is essential to the determination of pest control measures. In order to improve this process, here we propose a technique for automatically quantifying the damaged leaf area using k means clustering, which uses square Euclidian distances method for partition of leaf image.For grading of soybean leaf disese which appear on leaves based on segmented diseased region are done automatically by estiamting thae ratio of the unit pixel expressed under diseased region area and unit pixel expressed under Leaf region area.For experiment purpose samples of Bacterial Leaf Blight Septoria Brown spot, Bean Pod Mottle Virus infected soybean leaf images were taken for analysis.Finally estiamated diseased severity and its grading is compared with manual scoring based on conventional illustrated key diagram was conducted. Comparative assessment results showed a good agreement between the numbers of percentage scale grading obtained by manual scoring and by image analysis The result shows that the proposed method is precise and reliable than visual evaluation performed by patahlogiest.

34 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
2023149
2022154
2021104
202093
201952
201825