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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 1,722 Documents
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Dynamic domain transformation resource scheduling approach: water irrigation scheduling for urban farming Megat Nabil Irwan Megat Amerudin; Siti Khatijah Nor Abdul Rahim; Nasiroh Omar; Mohd Suffian Sulaiman; Amir Hamzah Jaafar; Raseeda Hamzah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp624-631

Abstract

Scheduling resources under limited resources using tailored approaches can be done successfully. However, there are situations and problems that require a schedule to handle uncertainties dynamically. The changes in the environment could lead to a non-optimal schedule, which could lead to the wastage of resources. The infeasible schedule could also be an outcome of changes that would render the schedule obsolete, and a new schedule must be generated. The majority of the scheduling problems are solved by a heuristic approach that utilizes a random number generator, thus the outcome is not guaranteed to be optimal. Domain transformation approach (DTA) is a scheduling methodology that has confirmed its expressive power in producing feasible and good quality schedules through avoidance of randomness elements as highly used in heuristic approaches. DTA has been employed in this study to solve the water irrigation scheduling for urban farming. The proposed model was tested on three different datasets. It was observed that the costs obtained on all datasets without utilizing the dynamic DTA are higher in all instances, which indicates that the solution produced by DTA is of higher quality. Thus, dynamic DTA is a more effective way of scheduling resources with considering ad-hoc changes.
Inspection based risk management of electric distribution overhead lines Adnan Hasan Tawafan; Dhafer Mayoof Alshadood; Fatima Kadhem Abd
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp1-12

Abstract

This paper introduces a comprehensive process based on inspection for determination of medium voltage (MV) overhead line condition and it was tried that all factors influencing the outage of distribution network integrated into one index that called condition index. A condition based failure rate model has been proposed and its unknown parameters are calculated based on historical data. Shortest path problem (SPP) model has been proposed for the long term scheduling of maintenance and reinvestment. Objective function includes sum of the reinvestment, maintenance costs, failure costs and energy not supplied (ENS) costs with considering budget and labor constraints. Finally, as a result of this research, optimal combination of various actions such as reinvestment, preventive maintenance (PM) and tree trimming and it’s scheduling has been determined over the ten and five-year horizon. Results confirmed acceptable performance of proposed method because of compliance with actual condition and engineering judgment.
Online news popularity prediction before publication: effect of readability, emotion, psycholinguistics features Suharshala Rajagopal; Anoop Kadan; Manjary Gangadharan Prappanadan; Lajish Vimala Lakshmanan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp539-545

Abstract

The development of world wide web with easy access to massive information sources anywhere and anytime paves way for more people to rely on online news media rather than print media. The scenario expedites rapid growth of online news industries and leads to substantial competitive pressure. In this work, we propose a set of hybrid features for online news popularity prediction before publication. Two categories of features extracted from news articles, the first being conventional features comprising metadata, temporal, contextual, and embedding vector features, and the second being enhanced features comprising readability, emotion, and psycholinguistics features are extracted from the articles. Apart from analyzing the effectiveness of conventional and enhanced features, we combine these features to come up with a set of hybrid features. We curate an Indian news dataset consisting of news articles from the most rated Indian news websites for the study and also contribute the dataset for future research. Evaluations are performed over the Indian news dataset (IND) and compared with the performance over the benchmark mashable dataset using various supervised machine learning models. Our results indicate that the proposed hybrid of enhanced features with conventional features are highly effective for online news popularity prediction before publication.
Seed of rice plant classification using coarse tree classifier Kim Wallie Vergara Geollegue; Edwin Romeroso Arboleda; Andy Agustin Dizon
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp727-735

Abstract

The goal of this paper is to help the agriculture to have consistent observation in the status of seeds in rice plants and have a good quality postproduction by classifying the seeds automatically leading to reduction of low-quality rice plants while achieving higher demands in exportation as the quality increases. Additionally, manually observing the seeds of rice plants does not give an accurate evaluation as factors such as fatigue and emotion can affect the result. Using image processing and color feature extraction, it extracted the red, green, and blue (RGB) color feature lying in the pixel point of the seed in the healthy and unhealthy images of rice plants and classified by coarse tree classifier (CTC). The classifier achieved a 100% accuracy and training time of 0.32189 seconds, hence the fitted machine learning approach in the study.
A comprehensive review on machine learning in agriculture domain Kavita Jhajharia; Pratistha Mathur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp753-763

Abstract

Agriculture is an essential part of sustaining human life. Population growth, climate change, resource competition are the key issues that increase food security and to handle such complex problems in agriculture production, intelligent or smart farming extends the incorporation of technology into traditional agriculture notion. Machine learning is a vitally used technology in agriculture to protect food security and sustainability. Crop yield production, water preservation, soil health and plant diseases can be addressed by machine learning. This paper has presented a compendious review of research papers that deployed machine learning in the agriculture domain. The observed sub-categories of the agriculture domain are crop yield prediction, soil management, pest management, weed management, and crop disease. The outcomes represent that machine learning provides better accuracy concerning classification or regression. Machine learning emerged with the internet of things, drones, robots, automated machinery, and satellite imagery motivates researchers for smart farming and food security.
Solving flexible job-shop scheduling problem using harmony search-based meerkat clan algorithm Muna Mohammed Jawad; Muhanad Tahrir Younis; Ahmed T. Sadiq
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp423-431

Abstract

The classical job shop scheduling (JSS) problem can be extended by allowing processing of an operation by any machine from a given set. This type of scheduling is known as flexible job shop scheduling (FJSS) problem. It incorporates all the difficulties and complexities of its predecessor classical problem. However, it is more complex as it is required to determine the assignment of operations to the machine. Swarm intelligence techniques proved their effectiveness in solving a wide range of complex NP-Hard real world problems. One of these techniques is the meerkat clan algorithm (MCA) that has been successfully applied to various optimization problems. This paper presents a modified MCA for solving the FJSS problem. The modification is based on using harmony search (HS). The introduction of HS provides more exploitation and intensification. HS generates various solutions, which are provided to the MCA. As a result, the exploitation of the local optimum is increased, which in turn increases the convergence rate. The experimental results show that the improved method achieves higher quality schedules. Additionally, the convergence rate is speeded up compared with the standalone algorithm. This gives the proposed method the superiority over the original algorithm.
Forward feature selection for toxic speech classification using support vector machine and random forest Agustinus Bimo Gumelar; Astri Yogatama; Derry Pramono Adi; Frismanda Frismanda; Indar Sugiarto
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp717-726

Abstract

This study describes the methods for eliminating irrelevant features in speech data to enhance toxic speech classification accuracy and reduce the complexity of the learning process. Therefore, the wrapper method is introduced to estimate the forward selection technique based on support vector machine (SVM) and random forest (RF) classifier algorithms. Eight main speech features were then extracted with derivatives consisting of 9 statistical sub-features from 72 features in the extraction process. Furthermore, Python is used to implement the classifier algorithm of 2,000 toxic data collected through the world's largest video sharing media, known as YouTube. Conclusively, this experiment shows that after the feature selection process, the classification performance using SVM and RF algorithms increases to an excellent extent. We were able to select 10 speech features out of 72 original feature sets using the forward feature selection method, with 99.5% classification accuracy using RF and 99.2% using SVM.
Privacy preserving human activity recognition framework using an optimized prediction algorithm Kambala Vijaya Kumar; Jonnadula Harikiran
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp254-264

Abstract

Human activity recognition, in computer vision research, is the area of growing interest as it has plethora of real-world applications. Inferring actions from one or more persons captured through a live video has its immense utility in the contemporary era. Same time, protecting privacy of humans is to be given paramount importance. Many researchers contributed towards this end leading to privacy preserving action recognition systems. However, having an optimized model that can withstand any adversary models that strives to disclose privacy information. To address this problem, we proposed an algorithm known optimized prediction algorithm for privacy preserving activity recognition (OPA-PPAR) based on deep neural networks. It anonymizes video content to have adaptive privacy model that defeats attacks from adversaries. The privacy model enhances the privacy of humans while permitting highly accurate approach towards action recognition. The algorithm is implemented to realize privacy preserving human activity recognition framework (PPHARF). The visual recognition of human actions is made using an underlying adversarial learning process where the anonymization is optimized to have an adaptive privacy model. A dataset named human metabolome database (HMDB51) is used for empirical study. Our experiments with using Python data science platform reveal that the OPA-PPAR outperforms existing methods.
Adaptive weight assignment scheme for multi-task learning Aminul Huq; Mst. Tasnim Pervin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp173-178

Abstract

Deep learning based models are used regularly in every applications nowadays. Gen- erally we train a single model on a single task. However, we can train multiple tasks on a single model under multi-task learning (MTL) settings. This provides us many benefits like lesser training time, training a single model for multiple tasks, reducing overfitting, and improving performances. To train a model in multi-task learning settings we need to sum the loss values from different tasks. In vanilla multi-task learning settings we assign equal weights but since not all tasks are of similar difficulty we need to allocate more weight to tasks which are more difficult. Also improper weight assignment reduces the performance of the model. We propose a simple weight assignment scheme in this paper which improves the performance of the model and puts more emphasis on difficult tasks. We tested our methods performance on both image and textual data and also compared performance against two popular weight assignment methods. Empirical results suggest that our proposed method achieves better results compared to other popular methods.
Estimation of closed hotels and restaurants in Jakarta as impact of corona virus disease spread using adaptive neuro fuzzy inference system Mohamad Yusak Anshori; Teay Shawyun; Dennis V. Madrigal; Dinita Rahmalia; Fajar Annas Susanto; Teguh Herlambang; Dieky Adzkiya
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp462-472

Abstract

Corona virus disease (COVID-19) have become a world health problem because they have attacked many people worldwide. Because this virus has spread massively in almost all countries, including Indonesia, the Indonesian government made some policies and rules to close down the hotels and restaurants to avoid the spread of COVID-19. Because of that, estimation of the number of closed down restaurants and hotels in Jakarta is vital for avoiding COVID-19 spreads further to other people, either domestic or foreign. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) is chosen as the estimation method. In estimating the number of closed restaurants and hotels using ANFIS, supporting variables such as the amount of casualties in Jakarta, the amount of casualties in Indonesia, and the amount of casualties in the world is required. As a result, ANFIS can estimate the amount of closed down restaurants and hotels approaching the target. The simulations are organized by partitioning the dataset into two parts: data of (80%) and data of testing (20%). According to ANFIS simulations, ANFIS can estimate the number of closed down restaurants and hotels in training data with optimal RMSE equals 0.5324 and testing data with optimal RMSE equals 5.3198.

Page 43 of 173 | Total Record : 1722

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