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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 45 Documents
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Search results for , issue "Vol 11, No 3: September 2022" : 45 Documents clear
Multi-objective optimization path planning with moving target Baraa M. Abed; Wesam M. Jasim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1184-1196

Abstract

Path planning or finding a collision-free path for mobile robots between starting position and its destination is a critical problem in robotics. This study is concerned with the multi objective optimization path planning problem of autonomous mobile robots with moving targets in dynamic environment, with three objectives considered: path security, length and smoothness. Three modules are presented in the study. The first module is to combine particle swarm optimization algorithm (PSO) with bat algorithm (BA). The purpose of PSO is to optimize two important parameters of BA algorithm to minimize distance and smooth the path. The second module is to convert the generated infeasible points into feasible ones using a new local search algorithm (LS). The third module obstacle detection and avoidance (ODA) algorithm is proposed to complete the path, which is triggered when the mobile robot detects obstacles in its field of vision. ODA algorithm based on simulating human walking in a dark room. Several simulations with varying scenarios are run to test the validity of the proposed solution. The results show that the mobile robots are able to travel clearly and completely safe with short path, proving the effectiveness of this method.  
Return on investment framework for profitable crop recommendation system by using optimized multilayer perceptron regressor Surekha Janrao; Deven Shah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp969-976

Abstract

Return on investment (ROI) plays very important role as a financial dimension in the agriculture sector. Many government agencies like Indian space research organization (ISRO), Indian council of agricultural research (ICAR), and Nitiayog are working on different agriculture projects to improve profitability and sustainability. This paper presents ROI framework to recommend more profitable crop to the farmers as per the current market price and demand which is missing in the existing crop recommendation system. Crop price prediction (CPP) and crop yield prediction (CYP) system are integrated in the ROI framework to predict more demandable crop to yield. This framework is designed by applying data analysis to provide regression statistics which further helps for model selection and improve the performance also. Optimized multilayer perceptron regressor algorithm has been evaluated through experimental results and it has been observed that it gives better performance as compared to other existing regression techniques.
Bio-inspired and deep learning approach for cerebral aneurysms prediction in healthcare environment Srividhya Srinivasa Raghavan; Arunachalam Arunachalam
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp872-877

Abstract

Diagnosis is being used in a variety of fields, including treatments, scientific knowledge, technology, industry, and many deals. A diagnosis begins with the person’s complaints and understanding something about the condition of the patient dynamically while in a question-and-answer session, as well as by taking measurements, like blood pressure or skin temperature, among other things. The prognosis is then calculated by considering the obtainable patient information. The adequate intervention is then prescribed, and the method may be repeated. In the medical field, humans, sometimes, have constraints when diagnosing diagnosis, primarily because this procedure is arbitrary and heavily relies on the assessor’s memories and perception of patient transmissions. The work is primarily concerned with the investigation of cerebellar aneurysm diagnosing. In the meantime, it’s become evident even during literature reviews research that a much more basis of theoretical research of a number of existing learning methods was required. As a result, this paper is to provide a comparison of classification techniques like tree structure, random trees, and regression. At about the same time, another important goal is to have a decision-making framework based on biomimetic elephant-whale enhancement for a great deal of consideration of cerebral aneurysm variables, providing a quick, accurate, and dependable clinical medicine remedy.
Machine learning modeling of power delivery networks with varying decoupling capacitors Yeong Kang Liew; Nur Syazreen Ahmad; Azniza Abd Aziz; Patrick Goh
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1049-1056

Abstract

This paper presents modeling of power delivery network (PDN) impedance with varying decoupling capacitor placements using machine learning techniques. The use of multilayer perceptron artificial neural networks (ANN) and gaussian process regression (GPR) techniques are explored, and the effects of the hyperparameters such as the number of hidden neurons in the ANN, and the choice of kernel functions in the GPR are investigated. The best performing networks in each case are selected and compared in terms of accuracy using test data consisting of PDN impedance responses that were never encountered during training. Results show that the GPR models were significantly more accurate than the ANN models, with an average mean absolute error of 5.23 mΩ compared to 11.33 mΩ for the ANN.
Bat with Firefly Hybridization for Optimal Allocation and Capacity of Distributed Generation in Distribution Network RajeshKumar Samala
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp%p

Abstract

This article grants an effective technique to find optimal allocation and capacity of Distributed Generation (DG) in a Radial Distribution Network (RDN). An effective method is a combination of Bat Approach (BA) and Firefly Approach (FA). Primarily, the load-flow investigation of RDS is performed by using Backward/ Forward (BW/FW) sweep algorithm. Because of load variation, the voltage profile, Voltage Stability Index (VSI) and power losses are investigated. Following that, weak buses are acknowledged on the basis of the VSI and power loss factors and fixing the DG. For recognizing the optimal location of DG, the Bat algorithm is used. After the DG placement, the power loss and VSI is diminished and maximized the voltage profile of the scheme. The Firefly Algorithm (FA) is used to assess the optimal capacity of the DG.  This projected technique is executed utilizing MATLAB/Simulink software and verified on IEEE 33 standard distribution scheme. Then efficiency of projected method is observed and compared with base case, Gravitational Search Approach (GSA) and Bat Approach.
Troop camouflage detection based on deep action learning Muslikhin Muslikhin; Aris Nasuha; Fatchul Arifin; Suprapto Suprapto; Anggun Winursito
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp859-871

Abstract

Detecting troop camouflage on the battlefield is crucial to beat or decide in critical situations to survive. This paper proposed a hybrid model based on deep action learning for camouflage recognition and detection. To involve deep action learning in this proposed system, deep learning based on you only look once (YOLOv3) with SquezeeNet and the fourth steps on action learning were engaged. Following the successful formulation of the learning cycle, an instrument examines the environment and performance in action learning with qualitative weightings; specific target detection experiments with view angle, target localization, and the firing point procedure were performed. For each deep action learning cycle, the complete process is divided into planning, acting, observing, and reflecting. If the results do not meet the minimal passing grade after the first cycle, the cycle will be repeated until the system succeeds in the firing point. Furthermore, this study found that deep action learning could enhance intelligence over earlier camouflage detection methods, while maintaining acceptable error rates. As a result, deep action learning could be used in armament systems if the environment is properly identified.
A new hybrid and optimized algorithm for drivers’ drowsiness detection Mouad Elmouzoun Elidrissi; Elmaati Essoukaki; Lhoucine Ben Taleb; Azeddine Mouhsen; Mohammed Harmouchi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1101-1107

Abstract

When the roads are monotonous, especially on the highways, the state of vigilance decreases and the state of drowsiness appears. Drowsiness is defined as the transitional phase from the awake to the sleepy state. However, In Morocco, the majority of fatal accidents on the highway are caused by drowsiness at the wheel, reaching 33.33% rate. Therefore, we proposed the conception and realization of an automatic method based on electroencephalogram (EEG) signals that can predict drowsiness in real time. The proposed work is based on time-frequency analysis of EEG signals from a single channel (FP1-Ref), and drowsiness is predicted using a personalized and optimized machine learning model (optimized decision tree classification method) under Python. The results are much significant and optimized, improving the accuracy from 95.7% to 96.4% and a time consuming from 0.065 to 0.053 seconds.
A novel evolutionary optimization algorithm based solution approach for portfolio selection problem Mohammad Shahid; Mohd Shamim; Zubair Ashraf; Mohd Shamim Ansari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp843-850

Abstract

The portfolio selection problem is one of the most common problems which drawn the attention of experts of the field in recent decades. The mean variance portfolio optimization aims to minimize variance (risk) and maximize the expected return. In case of linear constraints, the problem can be solved by variants of Markowitz. But many constraints such as cardinality, and transaction cost, make the problem so vital that conventional techniques are not good enough in giving efficient solutions. Stochastic fractal search (SFS) is a strong population based meta-heuristic approach that has derived from evolutionary computation (EC). In this paper, a novel portfolio selection model using SFS based optimization approach has been proposed to maximize Sharpe ratio. SFS is an evolutionary approach. This algorithm models the natural growth process using fractal theory. Performance evaluation has been conducted to determine the effectiveness of the model by making comparison with other state of art models such as genetic algorithm (GA) and simulated annealing (SA) on same objective and environment. The real datasets of the Bombay stock exchange (BSE) Sensex of Indian stock exchange have been taken in the study. Study reveals the superior performance of the SFS than GA and SA.
The feature extraction for classifying words on social media with the Naïve Bayes algorithm Arif Ridho Lubis; Mahyuddin Khairuddin Matyuso Nasution; Opim Salim Sitompul; Elviawaty Muisa Zamzami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1041-1048

Abstract

To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, pre-processing eliminates unnecessary words while feature extraction processes these words. This paper focuses on feature extraction in which calculations and searches are used by applying word2vec while in frequency using term frequency-Inverse document frequency (TF-IDF). The process of classifying words on Twitter with 1734 tweets which are defined as a document to weight the calculation of frequency with TF-IDF with words that often come out in tweet, the value of TF-IDF decreases and vice versa. Following the achievement of the weight value of the word in the tweet, the classification is carried out using Naïve Bayes with 1734 test data, yielding an accuracy of 88.8% in the Slack word category tweet and while in the tweet category of verb 78.79%. It can be concluded that the data in the form of words available on twitter can be classified and those that refer to slack words and verbs with a fairly good level of accuracy. so that it manifests from the habit of twitter social media user.
Pipe leakage detection system with artificial neural network Muhammad Iqmmal Rezzwan Radzman; Abd Kadir Mahamad; Siti Zarina Mohd Muji; Sharifah Saon; Mohd Anuaruddin Ahmadon; Shingo Yamaguchi; Muhammad Ikhsan Setiawan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp977-985

Abstract

This project aims to develop a system that can monitor to detect leaks in water distribution networks. It has been projected that leakage from pipelines may lead to significant economic losses and environmental damage. The loss of water from leaks in pipeline systems accounts for a large portion of the water supply. Pipelines are maintained throughout their lives span; however, it is difficult to avoid a leak occurring at some point. A tremendous amount of water could be saved globally if automated leakage detection systems were introduced. An embedded system that monitors water leaks can efficiently aid in water conservation. This project focuses on developing a real-time water leakage detection system using a few types of sensors: water flow rate sensor, vibration sensor, and water pressure sensor. The data from the sensors is uploaded and stored by the microcontroller (NodeMCU V3) to the database cloud (Google Sheets). The data that is stored in the database is analyzed by artificial neural network (ANN) by using Matlab software. An application is developed based on results from ANN training to detect the leakage event. Implementing the proposed system can increase operations efficiency, reduce delay times, and reduce maintenance costs after leaks are detected.

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