IAES International Journal of Artificial Intelligence (IJ-AI)
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.
Articles
40 Documents
Search results for
, issue
"Vol 11, No 2: June 2022"
:
40 Documents
clear
<![CDATA[Information technology based smart farming model development in agriculture land ]]>
<![CDATA[Al-Khowarizmi Al-Khowarizmi]]>;
<![CDATA[Arif Ridho Lubis]]>;
<![CDATA[Muharman Lubis]]>;
<![CDATA[Romi Fadillah Rahmat]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp564-571
<![CDATA[Smart farming in various worlds is not just about applying technology in terms of storing data on agricultural land. However, having a concept of measurable data based on available computational techniques trained and then generating knowledge. As an application, the agri drone sprayer can be used for the process of applying pesticides and liquid fertilizers on each side. In addition, drone surveillance is also useful in implementing smart farming such as mapping land so that farmers will know the condition of their agricultural land. However, the soil and weather sensor will also help the farmers to monitor the farmland as well. Devices with sensors can only obtain data in the form of air and soil humidity, temperature, soil pH, water content and forecasting the harvest period. So that the smart farming model can help farmers to get recommendations, in preventing the predicted damage to their land and crops. However, according to its geographical location, the application of smart farming can be a smart solution to agricultural problems in Indonesia and make the future of Indonesian Agriculture a technology-based smart agriculture.]]>
<![CDATA[Efficient de-noising technique for electroencephalogram signal processing ]]>
<![CDATA[Virupaxi Dalal]]>;
<![CDATA[Satish Bhairannawar]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp603-612
<![CDATA[An electroencephalogram (EEG) is a recording of various frequencies of electrical activity in the brain. EEG signal is very useful for diagnosis of various brain related diseases at early stage to prevent severe issues which may lead to loss of life. The raw EEG signal captured through the leads contain different type of noises which is not susceptible for diagnosis. In this paper, an efficient algorithm is proposed to process the raw EEG signal to combat the noise. To obtain noiseless EEG data, the likelihood test ratio is applied to interference computation block. The likelihood ratio test converts EEG data signal into segmented data with nearly constant noise characteristics. This will aid in detecting the noise present in a tiny segment which ensures proper signal denoising. The processed signal is compared with the database of noiseless EEG of the same person using principal component analysis (PCA) classifier. The proposed algorithm is 99.01% efficient to identify and combat noise in the EEG signal.]]>
<![CDATA[Dynamic domain transformation resource scheduling approach: water irrigation scheduling for urban farming ]]>
<![CDATA[Megat Nabil Irwan Megat Amerudin]]>;
<![CDATA[Siti Khatijah Nor Abdul Rahim]]>;
<![CDATA[Nasiroh Omar]]>;
<![CDATA[Mohd Suffian Sulaiman]]>;
<![CDATA[Amir Hamzah Jaafar]]>;
<![CDATA[Raseeda Hamzah]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp624-631
<![CDATA[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.]]>
<![CDATA[Online news popularity prediction before publication: effect of readability, emotion, psycholinguistics features ]]>
<![CDATA[Suharshala Rajagopal]]>;
<![CDATA[Anoop Kadan]]>;
<![CDATA[Manjary Gangadharan Prappanadan]]>;
<![CDATA[Lajish Vimala Lakshmanan]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp539-545
<![CDATA[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.]]>
<![CDATA[Seed of rice plant classification using coarse tree classifier ]]>
<![CDATA[Kim Wallie Vergara Geollegue]]>;
<![CDATA[Edwin Romeroso Arboleda]]>;
<![CDATA[Andy Agustin Dizon]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp727-735
<![CDATA[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.]]>
<![CDATA[A comprehensive review on machine learning in agriculture domain ]]>
<![CDATA[Kavita Jhajharia]]>;
<![CDATA[Pratistha Mathur]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp753-763
<![CDATA[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.]]>
<![CDATA[Solving flexible job-shop scheduling problem using harmony search-based meerkat clan algorithm ]]>
<![CDATA[Muna Mohammed Jawad]]>;
<![CDATA[Muhanad Tahrir Younis]]>;
<![CDATA[Ahmed T. Sadiq]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp423-431
<![CDATA[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.]]>
<![CDATA[Forward feature selection for toxic speech classification using support vector machine and random forest ]]>
<![CDATA[Agustinus Bimo Gumelar]]>;
<![CDATA[Astri Yogatama]]>;
<![CDATA[Derry Pramono Adi]]>;
<![CDATA[Frismanda Frismanda]]>;
<![CDATA[Indar Sugiarto]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp717-726
<![CDATA[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.]]>
<![CDATA[Estimation of closed hotels and restaurants in Jakarta as impact of corona virus disease spread using adaptive neuro fuzzy inference system ]]>
<![CDATA[Mohamad Yusak Anshori]]>;
<![CDATA[Teay Shawyun]]>;
<![CDATA[Dennis V. Madrigal]]>;
<![CDATA[Dinita Rahmalia]]>;
<![CDATA[Fajar Annas Susanto]]>;
<![CDATA[Teguh Herlambang]]>;
<![CDATA[Dieky Adzkiya]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp462-472
<![CDATA[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.]]>
<![CDATA[Change detection of pulmonary embolism using isomeric cluster and computer vision ]]>
<![CDATA[Mekala Srinivasa Rao]]>;
<![CDATA[Sagenela Vijaya Kumar]]>;
<![CDATA[Rambabu Pemula]]>;
<![CDATA[Anil Kumar Prathipati]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijai.v11.i2.pp787-798
<![CDATA[Visual change detection functions in X-ray analytics and computer vision attempt to divide X-ray images toward front and backside areas. There are various difficulties in change detection such as weather changes and shadows; real-time processing; intermittent object motion; lighting variation; and diverse object forms. Traditionally, this issue has been addressed via backdrop modeling methods and the creation of custom features. We present a new feature descriptor called pulmonary embolism detection using isomeric cluster (PEDIC), uses the concept of isomerism. The isomeric and cluster isomerism characteristics of the PEDIC are distinguish it from other graphs. At isomeric thetical orientations, the cluster pattern corresponds to consecutive differences in pixel intensity between the two images. Also, the clusters are oppositely orientated, and both clusters conform to a specified isomeric feature. The local area's lines and corner point information are identified and recorded using the PEDIC in several different directions. We introduced multiresolution PEDIC, which incorporates the multiresolution Gaussian filter to achieve increased resilience in the system. We expanded our research to include rotation-invariant characteristics. We also proposed inter-PEDIC and intra-PEDIC to identify motion changes in X-ray sequences, which allowed them to extract spatiotemporal characteristics.]]>
