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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 1,808 Documents
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Transfer-learning based skin cancer diagnosis using fine-tuned AlexNet by Marine Predators Algorithm Ibrahim Khaleel, Maha; Lakizadeh, Amir
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4822-4832

Abstract

Melanoma represents one of the most dangerous manifestations of skin cancer. According to statistics, 55% of patients with skin cancer have lost their lives as a result of this disease. Early diagnosis of this condition will significantly reduce mortality rates and save lives. In recent years, deep learning methods have shown promising results and captured the attention of researchers in this field. One common approach is the use of pre-trained deep neural networks. In this work, a pre-trained AlexNet networks, which are networks with specified architecture and weights is used to automatic skin melanoma diagnosis.  In the transfer learning phase, by reducing the learning rate, the pre-trained network is trained to recognize Skin cancer, which is called fine-tuning. In addition, Hyperparameters of the AlexNet network have been optimized by the Marine Predators Algorithm (MPA) to enhance the network performance. Experimental findings show the satisfactory efficiency of the presented approach, with an accuracy rate of 98.47%. The outcomes demonstrate the effectiveness of the suggested approach in contrast to alternative existing methods.
Levenberg-Marquardt-optimized neural network for rainfall forecasting Rudrappa, Gujanatti; Vijapur, Nataraj; Hosamane, Sateesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp182-192

Abstract

Rainfall is a crucial meteorological indicator with applications in agriculture, aviation, and military. Forecasting is essential due to unpredictable environmental changes. Current methods use complex statistical models, which are timeconsuming. The present study is targeted for forecasting rainfall with the help of meteorological parameters, viz., temperature, humidity, wind speed, wind direction, and rain, using a specialized artificial intelligence (AI) method and real-time data captured over the study area. The weather station installed at KLE Dr. M. S. Sheshgiri College of Engineering and Technology in Karnataka, India, collects meteorological data. The models used were principal component regression (PCR) and Levenberg-Marquardt -optimized neural network (LMAONN). The Levenberg-Marquardt (LMA) backpropagation (BP) algorithm performed better than other BP algorithms. The coefficient of determination (R2) observed for the PCR and LMAONN models were 0.57 and 0.87, respectively. The LMAONN model provided a better fit for rainfall forecasting than the PCR model, with an index of agreement (IoA) of 0.96, indicating good forecasting.
A systematic assertive wide-band routing using location and potential aware technique Mohammed Saifuddin, Karur; D. Devangavi, Geetha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3892-3899

Abstract

Delays occur when packets must be routed over several paths in a wireless sensor network with multiple origins and destinations. There are several causes, delays may occur everywhere, even in a multi-hop wireless network. Due to the broadcast nature of wireless networks, opportunistic routing was able to circumvent these delays. To avoid unnecessary delays, wide-band routing may be used to calculate the smaller path between two nodes. In this case, we address the shortcomings of the standard approach by taking into account the node's power. Path routing as well as the broadcast nature of wireless signals help mitigate the effects of shoddy wireless connections. The results show that the suggested approach outperformed the baseline in both end-to-end latency and packet delivery ratio.
Hybrid intrusion detection model for hierarchical wireless sensor network using federated learning Mani, Sathishkumar; Kishoreraja, Parasuram Chandrasekaran; Joseph, Christeena; Manoharan, Reji; Theerthagiri, Prasannavenkatesan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp492-499

Abstract

The applications of wireless sensor networks are vast and popular in today’s technology world. These networks consist of small, independent sensors that are capable of measuring various physical quantities. Deployment of wireless sensor networks increased due to immense applications which are susceptible to different types of attacks in an unprotected and open region. Intrusion detection systems (IDS) play a vital part in any secured environment for any network. IDS using federated learning have the potential to achieve better classification accuracy. Usually, all the data is stored in centralized server in order to communicate between the systems. On the other hand, federated learning is a distributed learning technique that does not transfer data but trains models locally and transfers the parameters to the centralized server. The proposed research uses a hybrid IDS for wireless sensor networks using federating learning. The detection takes place in real-time through detailed analysis of attacks at different levels in a decentralized manner. Hybrid IDS are designed for node level, cluster level and the base station where federated learning acts as a client and aggregated server.
Evaluation of distributed denial of service attacks detection in software defined networks S., Neethu; Aradhya, H. V. Ravish
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4488-4498

Abstract

Software-defined networking (SDN) revolutionizes networking by separating control logic and data forwarding, enhancing security against threats like distributed denial of service (DDoS) attacks. These attacks flood control plane bandwidth, causing SDN network failures. Recent studies emphasize the efficacy of machine learning (ML) and statistical approaches in identifying and mitigating these security risks. However, there has been a lack of focus on employing ensembling techniques, amalgamating diverse ML models, selecting pertinent features, and utilizing oversampling techniques to balance categorical data. Our study evaluates 20 machine-learning models, emphasizing feature engineering and addressing class imbalance using synthetic minority oversampling technique (SMOTE). The results indicate that ensemble methods such as light gradient boosting machine (LGBM) classifier, random forest classifier, XGB classifier, decision tree classifier obtained near-perfect scores (almost 100%) across all metrics, suggesting potential overfitting. Conversely, models like AdaBoost classifier, k-neighbors classifier, and support vector classifier (SVC) exhibited slightly lower (99%) but realistic performance, underscoring the intricacy of accurate prediction in cybersecurity. Simpler models, including logistic regression, linear discriminant analysis, and Gaussian naive Bayes, demonstrated moderate to low accuracy, approximately around 70%. These findings stress the imperative need for a nuanced approach in the selection and fine-tuning of ML models to ensure effective DDoS detection in SDN environments. 
Detection and avoidance of black-hole attack in mobile adhoc network using bee-ad-hoc on-demand distance vector Pala, Srikanth; Maddula, Prasad; Pokkuluri, Kiran Sree; Pattem, Sunil; Kurada, Ramachandra Rao; Yadavalli, Ramu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp822-832

Abstract

Mobile adhoc networks (MANETs) are self-configuring networks with a dynamic infrastructure suit for real world applications. Due to the exponential increase in the network devices an efficient routing algorithm for dynamic network adhering the security issues is a critical challenge needs to be addressed. This article attempts to address this issue with the implemention of ad-hoc on-demand distance vector (AODV) routing approach, which is the best of its kind in the dynamic network design of MANETs. The primary goal is to address security attack weaknesses through the implementation of dynamic topologies and reactive routing. To this end, a bio-inspired swarm intelligence algorithm called Bees algorithm is used to emulate the AODV technique. In order to provide a lightweight solution that integrates the Bee algorithm and AODV routing, this study presents a unique algorithm called Bee-AODC. The proposed Bee-AODC algorithm possess the both the AODV's dynamic topology construction capabilities and the Bee algorithm's foraging strategy which effectively address security weaknesses by creating a dynamic network topology for ad hoc routing. By using the suggested Bee-AODC algorithm instead of the traditional AODV routing method, throughput is increased by 12.87% while packet loss, latency, and energy consumption are reduced by 20%, 40%, and 18%, respectively.
Implementation of fuzzy logic approach for thalassemia screening in children Redy Susanto, Erliyan; Syarif, Admi; Warsito, Warsito; Nisa Berawi, Khairun; Ayu Sangging, Putu Ristyaning; Wantoro, Agus
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4062-4070

Abstract

Thalassemia is one of the most dangerous blood disorders that can lead to severe complications. It is an inherited disease, usually detected after a child is two to four years old. Identification of thalassemia is a complex task, involving many variables. Doctors generally diagnose thalassemia by using a complete blood count (CBC) and high-performance liquid chromatography (HPLC) test results. However, HPLC tests are expensive and time consuming, hence the need for other methods to identify thalassemia. There are many studies on the application of artificial intelligence for medical applications. In this study, we developed a new fuzzy-based approach to identify thalassemia based on a patient’s blood laboratory results. First, we analyzed the CBC data for blood disorder prediction. Secondly, we adopt the test results of peripheral blood smear (PBS) to identify whether the person has thalassemia. We conducted several experiments using 30 (thirty) hospital patient data and the results were compared with the results provided by experts. The experimental results show that the system can determine blood disorders with 93% accuracy and 100% precision in thalassemia prediction. This system is very effective to help doctors in diagnosing thalassemia patients.
Rice quality classification system using convolutional neural network and an adaptive neuro-fuzzy inference system Kamelia, Lia; Zaki Hamidi, Eki Ahmad; Muhammad Fadilla, Reno
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4113-4120

Abstract

In the food sector, rice processing and classification are essential operations that help maintain strict quality and safety standards, satisfy various consumer preferences, and satisfy particular market demands. Artificial intelligence (AI) and machine learning techniques are used in automated systems to reliably and effectively classify rice quality. This research compares a rice quality classification system using a convolutional neural network (CNN) and an adaptive neuro-fuzzy inference system (ANFIS). Both methods are evaluated for their ability to classify rice based on quality, utilizing a dataset encompassing various physical characteristics. The comparative analysis results reveal the strengths and weaknesses of each approach in addressing this classification task. In this research, two classification systems for different varieties of rice-medium and premium—are compared. CNN and ANFIS are the techniques applied. The CNN accuracy on the rice picture is 62.5%. Thus, a contrast enhancement procedure was applied and had better accuracy at 75%. However, when contrasted with the classification made using the ANFIS approach, the ANFIS method continued to yield the best accuracy, 82.25%.
Predicting enhanced diagnostic models: deep learning for multi-label retinal disease classification Sundararajan, Sridhevi; Ramachandran, Harikrishnan; Gupta, Harshita; Patil, Yashraj
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp54-61

Abstract

In this study, we assess three convolutional neural network (CNN) architectures—VGG16, ResNet50, and InceptionV3 for multi classification of fundus images in the retinal fundus multi-disease image dataset (RFMID2), comprising of 860 images. Focusing on diabetic retinopathy, exudation, and hemorrhagic retinopathy, we preprocessed the dataset for uniformity and balance. Using transfer learning, the models were adapted for feature extraction and fine-tuned to our multi-label classification task. Their performance was measured by subset accuracy, precision, recall, F1-score, hamming loss, and Jaccard score. VGG16 emerged as the top performer, with the highest subset accuracy (84.81%) and macro precision (95.83%), indicating its superior class distinction capabilities. ResNet50 showed commendable accuracy (79.75%) and precision (86.70%), whereas InceptionV3 lagged with lower accuracy (66.67%) and precision (81.21%). These findings suggest VGG16’s depth offers advantages in multi-label classification, highlighting InceptionV3’s limitations in complex scenarios. This analysis helps optimize CNN architecture selection for specific tasks, suggesting future exploration of dataset variability, ensemble methods, and hybrid models for improved performance.
A novel pairwise based convolutional neural network for image preprocessing enhancement Ravi, Chaitra; Gaddadevara Matt, Siddesh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4095-4105

Abstract

Wildfires are untamable and devastating forces that impact both urban and rural regions. While predicting wildfires is challenging, efforts are made to mitigate the damage they inflict. The previous researches have limitations such as not being able to find a small region of fire in the dataset. In this research, pairwise region-based convolutional neural network (PR-CNN) is proposed for wildfire detection. The dataset used for wildfire detection is the fire luminosity airborne-based machine learning evaluation (FLAME) dataset that is pre-processed through normalization and hue, saturation, and lightness (HSV) color space to improve the image quality. Pre-processed images are taken as input to region-based convolutional neural network (R-CNN) for detection, the R-CNN has a region proposal layer that is enhanced by pairwise region and named PR-CNN. These wrapped images are fed into CNN architecture to extract and features to detect wildfire. Additionally, post processing technique like soft-non-maximum suppression (NMS) is utilized to eliminate the duplicate detection from PR-CNN for enhancing the detection accuracy. The proposed method achieves a higher accuracy of 97.44%, a precision of 97.32%, recall of 97.31%, and f1-score of 96.67%, which is comparatively superior to the existing algorithms like recurrent neural network (RNN), and R-CNN.

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