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IAES International Journal of Artificial Intelligence (IJ-AI)
Published by Institute of Advanced Engineering and Science
ISSN : 20894872     EISSN : 22528938     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
IAES International Journal of Artificial Intelligence (IJ-AI) publishes articles in the field of artificial intelligence (AI). The scope covers all artificial intelligence area and its application in the following topics: neural networks; fuzzy logic; simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc); reasoning and evolution; intelligence applications; computer vision and speech understanding; multimedia and cognitive informatics, data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning; technology and computing (like particle swarm optimization); intelligent system architectures; knowledge representation; bioinformatics; natural language processing; multiagent systems; etc.
Arjuna Subject : -
Articles 120 Documents
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Search results for , issue "Vol 13, No 1: March 2024" : 120 Documents clear
An automated speech recognition and feature selection approach based on improved Northern Goshawk optimization Suryakumar, Santosh Kumar; Hiremath, Bharathi S.; Mohankumar, Nageswara Guptha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp296-304

Abstract

Automatic speech recognition (ASR) approach is dependent on optimal speech feature extraction, which attempts to get a parametric depiction of an input speech signal. Feature extraction (FE) strategy combined with a feature selection (FS) approach should capture the most important features of the signal while discarding the rest. FS is a crucial process that can affect the pattern classification and recognition system's performance. In this research, we introduce a hybrid supervised learning using metaheuristic technique for optimum FE and FS termed Northern Goshawk optimization (NGO) and opposition-based learning (OBL). Pre-processing, feature extraction and selection, and recognition are the three steps of the proposed technique. The pre-processing is done first to lessen the amount of noise. In the FE stage, we extract features. The OBL-NGO method is used to pick the best collection of extracted characteristics. Finally, these optimised features are utilised to train the k-nearest neighbour (KNN) classifier, and the matching text is shown as the output based on these optimised characteristics of the provided input audio signal. The system's performance is outstanding, and the suggested OBL-NGO is best suited for ASR, according to the testing data.
Internet of things in public healthcare organizations: the mediating role of attitude Noor, Bashar Dheyaa; Katheeth, Zainab Dalaf; Noor, Ammar Dheyaa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp57-65

Abstract

Internet of things (IoT) is a promising technology to face the challenges of COVID19 and enhance the capacities of public hospitals. However, few of the literature examined the behavioural intention (BI) of patients to use the IoT wearable health device (IoTWHD). This paper aims to examine the factor that affect the BI toward using the IoTWHD. The study proposes that variables of Technology acceptance model (TAM3) along with unified theory of acceptance and use of technology (UTAUT) can explain the BI. The population is the patients of public hospitals. Convivence sampling was deployed to collect the data using a questionnaire. 161 respondents participated in this study. The finding of Smart Partial Least Square showed that subjective norms (SN) affected the perceived usefulness (PU). Perceived enjoyment (PE) affected the perceived ease of use (PEOU). Further, PU, PEOU and perceived security (PS) affected the BI to use IoTWHD. Attitude mediated the effect of PU and PEOU on BI. More positive word of mouth are needed to enhance the perception of patients about BI to use IoTWHD in public health organizations.
Sophisticated face mask dataset: a novel dataset for effective coronavirus disease surveillance Ul Haque, Sheikh Burhan; Wani, Mohd Hanief
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1030-1037

Abstract

Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Machine learning for text document classification-efficient classification approach Mohammed Ali, Sura I.; Nihad, Marwah; Mohamed Sharaf, Hussien; Farouk, Haitham
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp703-710

Abstract

Numerous alternative methods for text classification have been created because of the increase in the amount of online text information available. The cosine similarity classifier is the most extensively utilized simple and efficient approach. It improves text classification performance. It is combined with estimated values provided by conventional classifiers such as Multinomial Naive Bayesian (MNB). Consequently, combining the similarity between a test document and a category with the estimated value for the category enhances the performance of the classifier. This approach provides a text document categorization method that is both efficient and effective. In addition, methods for determining the proper relationship between a set of words in a document and its document categorization is also obtained.
Deep learning method for lung cancer identification and classification Jamdar, Sahil; Vaddin, Jayashree; B. Nargundkar, Sachidanand; Patil, Shrinivasa
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp1119-1128

Abstract

Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Photo-realistic photo synthesis using improved conditional generative adversarial networks Mandara Kirimanjeshwara, Raghavendra Shetty; Prasad, Sarappadi Narasimha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp516-523

Abstract

There are a wide range of potential uses for both the forward (generating face drawings from actual images) and backward (generating photos from synthetic face sketches). However, photo/sketch synthesis is still a difficult problem to solve because of the distinct differences between photos and sketches. Existing frameworks often struggle to acquire a strong mapping among the geometry of drawing and its corresponding photo-realistic pictures because of the little amount of paired sketch-photo training data available. In this study, we adopt the perspective that this is an image-to-image translation issue and investigate the usage of the well-known enhanced pix2pix generative adversarial networks (GANs) to generate high-quality photo-realistic pictures from drawings; we make use of three distinct datasets. While recent GAN-based approaches have shown promise in image translation, they still struggle to produce high-resolution, photorealistic pictures. This technique uses supervised learning to train the generator's hidden layers to produce low-resolution pictures initially, then uses the network's implicit refinement to produce high-resolution images. Extensive tests on three sketch-photo datasets (two publicly accessible and one we produced) are used to evaluate. Our solution outperforms existing image translation techniques by producing more photorealistic visuals with a peak signal-to-noise ratio of 59.85% and pixel accuracy of 82.7%. 
Empowering anomaly detection algorithm: a review Iqbal Basheer, Muhammad Yunus; Mohd Ali, Azliza; Osman, Rozianawaty; Abdul Hamid, Nurzeatul Hamimah; Nordin, Sharifalillah; Mohd Ariffin, Muhammad Azizi; Iglesias Martínez, José Antonio
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp9-22

Abstract

Detecting anomalies in a data stream relevant to domains like intrusion detection, fraud detection, security in sensor networks, or event detection in internet of things (IoT) environments is a growing field of research. For instance, the use of surveillance cameras installed everywhere that is usually governed by human experts. However, when many cameras are involved, more human expertise is needed, thus making it expensive. Hence, researchers worldwide are trying to invent the best-automated algorithm to detect abnormal behavior using real-time data. The designed algorithm for this purpose may contain gaps that could differentiate the qualities in specific domains. Therefore, this study presents a review of anomaly detection algorithms, introducing the gap that presents the advantages and disadvantages of these algorithms. Since many works of literature were reviewed in this review, it is expected to aid researchers in closing this gap in the future.
Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques Kumar Napa, Komal; Kalyan Kumar, Angati; Murugan, Sangeetha; Mahammad, Kamaluru; Admassu Assegie, Tsehay
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp730-736

Abstract

Chronic heart disease (CHD) is a common complication among patients suffering in the cardiological intensive care unit, often resulting in poor prognosis and high mortality. Early prediction of CHD can reduce mortality by preventing the severity of the disease. This study evaluated the efficacy of on recursive feature elimination for predicting CHD using supervised learning techniques for predicting CHD. The study employed 1190 Cleveland Hungarian CHD dataset. Different supervised learning techniques (support vector machine, decision tree, k-nearest neighbor, Naive Bayes, stochastic gradient descent, adaptive boosting, and multilayer perceptron) were used to study the efficacy of the recursive feature elimination. Chest pain type, sex, blood sugar level, angina, depression, and slope were associated with CHD occurrence. The accuracy of the K-nearest neighbor and decision tree model was 89.91% for the feature-selected dataset indicating good predictive ability. Ultimately, the support vector machine and logistic regression with the selected features exhibited good discriminatory ability for early prediction of CHD. Thus, the recursive feature elimination is a good approach to develop a a model with higher accuracy to predict CHD.
Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset Chimphlee, Witcha; Chimphlee, Siriporn
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp817-826

Abstract

With the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CIC-IDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
Anomaly detection using deep learning based model with feature attention Nayak, Rikin J.; Chaudhari, Jitendra P.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp383-390

Abstract

Anomaly detection is a difficult problem with numerous industrial applications, such as analyzing the quality of objects using images. Anomaly detection is the process of identifying outliers in a given dataset. Recently, machine learning approaches to computer vision problems have outperformed classical state-of-the-art approaches. Anomaly detection problems can be solved using supervised approaches. However, labelled datasets are hard to obtain. Thus, many researchers have taken an unsupervised approach to solving the problem of anomaly detection. In this study, we use an adversarial auto encoder model as a base model and create a custom model to detect anomalies in images and videos. The model was trained exclusively on normal data. The modified national institute of standards and technology database (MNIST) dataset achieved an area under curve (AUC) score of 0.872 for anomaly detection, while the University of California San Diego (UCSD) anomaly dataset (Video dataset) achieved an AUC score of 0.74 for Ped1 and 0.87 for Ped2. To calculate the anomaly score, the concept of attention weights is combined with the reconstruction loss, and the proposed method outperformed other similar methods designed for the same problem. However, the usefulness of the proposed model was demonstrated through the detection of anomalies, and the model is still being improved for use in real-world situations.

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