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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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Optimization scheme for intelligent master controller with collaboratives energy system Jack, Kufre Esenowo; Olubiwe, Matthew; Chibuzo Obichere, Jude-Kenndey; Onyema, Akwukwaegbu Isdore; Nosiri, Onyebuchi C.; Chijioke, Joe-Uzuegbu
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.pp236-246

Abstract

This paper explores the use of deep learning to optimize the performance of a peer-to-peer energy system with an intelligent master controller. The goal addresses inefficiencies caused by energy seasonality by predicting hourly power consumption through a deep learning algorithm. The intelligent master controller was designed to manage the collaborative energy system, and the deep learning technique was employed as an optimization scheme to forecast power system performance for more efficient utilization. The deep learning algorithm was trained using dataset from American electric power, where consumer load data serves as input, and forecasted power serves as output. The forecasted power was then used as input to the intelligent master controller, which determines suitable power supply for generation and storage based on the predicted demand. The experiment results show promising accuracy with a root mean square error (RMSE) of 0.1819 for hourly energy consumption averaged over a year, 0.2419 for hourly energy consumption averaged over a month, 0.0662 for hourly energy consumption averaged per day, and 0.0217 for hourly energy consumption. These findings demonstrate that the system is well-trained and capable of accurately predicting the energy required by the intelligent master controller, thus enhancing the overall performance of the peer-to-peer energy system.
Artificial intelligence research in Nigeria: Topic modelling and scientometric analysis Tolulope, Afolabi Ibukun; Isaac, Martins; Timileyin, Owoseni; Seth, Samuel; Kingsley, Oputa
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.pp597-609

Abstract

In developing countries such as Nigeria, artificial intelligence (AI) research has the potential to drive rapid advancement in various aspects of development, including the economy and technology. However, it is crucial to understand the focus of Nigerian AI researchers and identify unexplored areas of research that could lead to unprecedented development. To address this need, we used natural language processing, machine learning, and statistical algorithms to investigate the main areas of interest of Nigerian AI researchers. We identified ten topics and used scientometric analyses to reveal key concepts, keyword co-occurrences, and authorship networks. Our study found that Covenant University was the most prolific institution, with 375 publications, followed by the Federal University of Technology with 135 publications and the University of Ibadan with 121 publications. Overall, our research provides valuable insights into the structure and progression of AI research in Nigeria and highlights areas for improvement.
Harnessing the power of blockchain technology to support decision-making in e-commerce processes Al-Moghrabi, Khaldun G.; Al-Ghonmein, Ali M.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1380-1387

Abstract

Technology, such as blockchain, has emerged as a promising solution for addressing the challenges of e-commerce decision-making. In this study, we explore the potential benefits of integrating blockchain technology into e-commerce and its role in supporting decision-making in e-commerce. We also examine blockchain’s benefits in terms of enhanced security, transparency, and efficiency for e-commerce platforms. Furthermore, the study discusses the challenges of implementing blockchain for e-commerce, including scalability, integration, regulatory frameworks, user experience, privacy, interoperability, and sustainability. By analyzing these challenges, the study provides valuable insights for future research and development efforts to facilitate a seamless adoption of blockchain technology in e-commerce decisions. Blockchain technology holds the potential to transform an e-commerce ecosystem by overcoming these challenges and unlocking its transformative potential.
A new optimal strategy for energy minimization in wireless sensor networks Ouchitachen, Hicham; Darif, Anouar; Er-rouidi, Mohamed; Johri, Mustapha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2265-2274

Abstract

In recent years, evolutionary and metaheuristic algorithms have emerged as crucial tools for optimization in the field of artificial intelligence. These algorithms have the potential to revolutionize various aspects of our lives by leveraging the multidisciplinary nature of wireless sensor networks (WSNs). This study aims to introduce genetic and simulated annealing algorithms as effective solutions for enhancing WSN performance. Our contribution entails two main phases. Firstly, we establish mathematical models and formulate objectives as a nonlinear constrained optimization problem. Secondly, we develop two algorithmic solutions to address the formulated optimization problem. The obtained results from multiple simulations demonstrate the positive impact of the proposed strategies on improving network performance in terms of energy consumption.
Segmentation and yield count of an arecanut bunch using deep learning techniques Arekattedoddi Chikkalingaiah, Anitha; Dhanesha, RudraNaik; Chikkathore Palya Laxmana, Shrinivasa Naika; Neelegowda, Krishna Alabujanahalli; Mangala Puttaswamy, Anirudh; Ayengar, Pushkar
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.pp542-553

Abstract

Arecanut is one of Southeast Asia’s most significant commercial crops. This work aims at helping arecanut farmers get an estimate of the yield of their orchards. This paper presents deep-learning-based methods for segmenting arecanut bunch from the images and yield estimation. Segmentation is a fundamental task in any vision-based system for crop growth monitoring and is done using U-Net squared model. The yield of the crop is estimated using Yolov4. Experiments were done to measure the performance and compared with benchmark segmentation and yield estimation with other commodities, as there were no benchmarks for the arecanut. U-Net squared model has achieved a training accuracy of 88% and validation accuracy of 85%. Yolo shows excellent performance of 94.7% accuracy for segmented images, which is very good compared to similar crops.
A three-step combination strategy for addressing outliers and class imbalance in software defect prediction Rizky Pribadi, Muhammad; Dwi Purnomo, Hindriyanto; Hendry, Hendry
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2987-2998

Abstract

Software defect prediction often involves datasets with imbalanced distributions where one or more classes are underrepresented, referred to as the minority class, while other classes are overrepresented, known as the majority class. This imbalance can hinder accurate predictions of the minority class, leading to misclassification. While the synthetic minority oversampling technique (SMOTE) is a widely used approach to address imbalanced learning data, it can inadvertently generate synthetic minority samples that resemble the majority class and are considered outliers. This study aims to enhance SMOTE by integrating it with an efficient algorithm designed to identify outliers among synthetic minority samples. The resulting method, called reduced outliers (RO)-SMOTE, is evaluated using an imbalanced dataset, and its performance is compared to that of SMOTE. RO-SMOTE first performs oversampling on the training data using SMOTE to balance the dataset. Next, it applies the mining outlier algorithm to detect and eliminate outliers. Finally, RO-SMOTE applies SMOTE again to rebalance the dataset before introducing it to the underlying classifier. The experimental results demonstrate that RO-SMOTE achieves higher accuracy, precision, recall, F1-score, and area under curve (AUC) values compared to SMOTE.
Enhancing aerial image registration: outlier filtering through feature classification Merza, Hayder Mosa; Sbeity, Ihab; Dbouk, Mohamed; Ibrahim, Zein Al Abidin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1900-1912

Abstract

In the context of feature-based image registration, the crucial task of outlier removal plays a pivotal role in achieving precise registration accuracy. This research introduces an innovative binary classifier founded on an adaptive approach for effectively identifying and eliminating outliers. The methodology begins with the utilization of the scale invariant feature transform (SIFT) to extract features from two images, initially matched using the Euclidian distance metrics. Subsequently, a classification procedure is executed to segregate the feature points into two categories: genuine matches (inliers) and spurious matches (outliers), which is accomplished through the brute-force matcher (BFM) technique. To enhance this process further, a novel classifier rooted in the random forest algorithm is introduced. This classifier is trained and tested using a comprehensive dataset curated for this study. The newly proposed classifier plays a pivotal role in attenuating the influence of outliers, ultimately leading to refined image registration process characterized by enhanced accuracy. The effectiveness of this outlier removal approach is assessed through a meticulous analysis of positional and classification accuracy. Additionally, we offer comparative insights by evaluating the performance of selected algorithm on our dataset.
EASESUM: an online abstractive and extractive text summarizer using deep learning technique Adeniyi, Jide Kehinde; Ajagbe, Sunday Adeola; Adeniyi, Abidemi Emmanuel; Aworinde, Halleluyah Oluwatobi; Falola, Peace Busola; Adigun, Matthew Olusegun
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1888-1899

Abstract

Large volumes of information are generated daily, making it challenging to manage such information. This is due to redundancy and the type of data available, most of which needs to be more structured and increases the amount of search time. Text summarization systems are considered a real solution to this vast amount of data because they are used for document compression and reduction. Text summarization keeps the relevant information and eliminates the text's non-relevant parts. This study uses two types of summarizers: Extractive Text summarizers and Abstractive text summarizers. The Text Rank Algorithm was used to implement the Extractive summarizer, while Bi-directional Recurrent Neural Network (RNN) was used to implement the Abstractive text summarizer. To improve the quality of summaries produced, word embedding was also used. For the evaluation of the summarizers, the ROUGE evaluation system was used. ROUGE contrasts summaries created by hand versus those created automatically. ROUGE examination of the produced summary revealed the superiority of human-produced summaries over those generated automatically. For this paper, a summarizer was implemented as a Web Application. The average ROUGE recall score ranging from 30.00 to 60.00 for abstractive summarizer and 0.75 to 0.82 for extractive text showed an encouraging result.
Automated signal pre-emption system for emergency vehicles using internet of things Sothenahalli Krishnaraju, Pushpa; Thimmasandra Narayanappa, Manjunath; Kathavate, Sheela; Shirwaikar, Rudresh; Chandrakanth Rao, Anupchandra Rao; Rohith, Jayachnadra
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.pp899-908

Abstract

Vehicle administration systems are one of the major highlights especially in urban areas. One important critical component that requires attention are signal preemption systems. Every single work on traffic congestion identification either requires prior learning or long time to distinguish and perceive the closeness of congestion. FutureSight performs predictive analysis and control of traffic signals through the application of machine learning to aide ambulances in such a way that, a signal turns green beforehand so as to ensure an obstacle free path to the ambulance from source to destination based on various parameters such as traffic density, congestion length, previous wait times, arrival time thereby eliminating the need for human intervention. The method allows flexible interface to the driver to enter the hospital details to reach the destination with in time. The app then plans out the fastest route from the pickup spot to the selected hospital and sends this route to the system. The system then predict the amount of time that is required by the signal to remain green so as to clear all traffic at that specific junction before the ambulance arrives at that location.
Advanced mask region-based convolutional neural network based deep-learning model for lung cancer detection Krishna, Bhavani; Madigondanahalli Thimmaiah, Gopalakrishna
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.pp1179-1186

Abstract

Millions of individuals are affected each year by lung cancer, a serious global health concern. It may also cause numerous potentially fatal pulmonary problems, including infections, hemorrhage, or collapse. Finding a consistent and an effective way to ascertain lung cancer using medical imaging techniques is one of the primary issues in medical image processing. The difficulty of this task stems from the fact that the regions of the lungs that are affected by cancer might differ greatly in expressions of their size, location, shape, and aesthetics. Identifying whether the identified area is benign (non-cancerous) or malignant (cancerous) is another difficult task. Finding the appropriate course of treatment for the patient will depend on this. A critical stage in the identification of lung malignancy is identifying the knobs that are expected to be malevolent. To solve these issues, in this study work we employ a deep learning methodology based on Mask region-based convolutional neural network (Mask-RCNN). For the purpose of identifying and locating infected lung regions on computed tomography (CT) scan images, model is built utilizing the customized Mask-RCNN. In accordance with the evaluation's findings, the model scored 99.32% for accuracy and 99.45% for mean DICE, respectively.

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