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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN : 20894872     EISSN : 22528938     DOI : -
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.
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Articles 744 Documents
Deep learning intrusion detection system for mobile ad hoc networks against flooding attacks Oussama Sbai; Mohamed Elboukhari
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.pp878-885

Abstract

Mobile ad hoc networks (MANETs) are infrastructure-less, dynamic wireless networks and self-configuring, in which the nodes are resource constrained. With the exponential evolution of the paradigm of smart homes, smart cities, smart logistics, internet of things (IoT) and internet of vehicle (IoV), MANETs and their networks family, such as flying ad-hoc networks (FANETs), vehicular ad-hoc networks (VANETs), and wireless sensor network (WSN), are the backbone of the whole networks. Because of their multitude use, MANETs are vulnerable to various attacks, so intrusion detection systems (IDS) are used in MANETs to keep an eye on activities in order to spot any intrusions into networks. In this paper, we propose a knowledge-based intrusion detection system (KBIDS) to secure MANETs from two classes of distributed denial of service (DDoS) attacks, which are UDP/data and SYN flooding attacks. We use the approach of deep learning exactly deep neural network (DNN) with CICDDoS2019 dataset. Simulation results obtained show that the proposed architecture model can attain very interesting and encouraging performance and results (Accuracy, Precision, Recall and F1-score).
Depression prediction using machine learning: a review Hanis Diyana Abdul Rahimapandi; Ruhaila Maskat; Ramli Musa; Norizah Ardi
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.pp1108-1118

Abstract

Predicting depression can mitigate tragedies. Numerous works have been proposed so far using machine learning algorithms. This paper reviews publications from online electronic databases from 2016 to 2020 that use machine learning techniques to predict depression. The aim of this study is to identify important variables used in depression prediction, recent depression screening tools adopted, and the latest machine learning algorithms used. This understanding provides researchers with the fundamental components essential to predict depression. Fifteen articles were found relevant. We based our review on the systematic mapping study (SMS) method. Three research questions were answered through this review. We discovered that sixteen variables were deemed important by the literature. Not all of the reviewed literature utilizes depression screening tools in the prediction process. Nevertheless, from the five screening tools discovered, the most frequently used were hospital anxiety and depression scale (HADS) and hamilton depression rating scale (HDRS) for general population, while for literature targeting older population geriatric depression scale (GDS) was often employed. A total of twenty-two machine learning algorithms were identified employed to predict depression and random forest was found to be the most reliable algorithm across the publications.
A text mining and topic modeling based bibliometric exploration of information science research Tipawan Silwattananusarn; Pachisa Kulkanjanapiban
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.pp1057-1065

Abstract

This study investigates the evolution of information science research based on bibliometric analysis and semantic mining. The study discusses the value and application of metadata tagging and topic modeling. Forty-two thousand seven hundred thirty-eight articles were extracted from Clarivate Analytic's Web of Science Core Collection 2010-2020. This study was divided into two phases. Firstly, bibliometric analyzes were performed with VOSviewer. Secondly, the topic identification and evolution trends of information science research were conducted through the topic modeling approach latent dirichlet allocation (LDA) is often used to extract themes from a corpus, and the topic model was a representation of a collection of documents that is simplified using topic-modeling-toolkit (TMT). The top 10 core topics (tags) were information research design, information health-based, model data public, study information studies, analysis effect implications, knowledge support web, data research, social research study, study media information, and research impact time for the studied period. Not only does topic modeling assist in identifying popular topics or related areas within a researcher's area, but it may be used to discover emerging topics or areas of study throughout time.
A machine learning approach for Bengali handwritten vowel character recognition Shahrukh Ahsan; Shah Tarik Nawaz; Talha Bin Sarwar; M. Saef Ullah Miah; Abhijit Bhowmik
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.pp1143-1152

Abstract

Recognition of handwritten characters is complex because of the different shapes and numbers of characters. Many handwritten character recognition strategies have been proposed for both English and other major dialects. Bengali is generally considered the fifth most spoken local language in the world. It is the official and most widely spoken language of Bangladesh and the second most widely spoken among the 22 posted dialects of India. To improve the recognition of handwritten Bengali characters, we developed a different approach in this study using face mapping. It is quite effective in distinguishing different characters. The real highlight is that the recognition results are more efficient than expected with a simple machine learning technique. The proposed method uses the Python library Scikit-Learn, including NumPy, Pandas, Matplotlib, and support vector machine (SVM) classifier. The proposed model uses a dataset derived from the BanglaLekha isolated dataset for the training and testing part. The new approach shows positive results and looks promising. It showed accuracy up to 94% for a particular character and 91% on average for all characters.
Four inputs-one output fuzzy logic system for washing machine Nurain Zulaikha Husin; Muhammad Zaini Ahmad; Mustafa Mamat
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.pp819-825

Abstract

The presence of fuzzy logic system on washing machine becomes a demand in every home as it simplifies human work. The uses of washing machine will facilitate the user, reduce electricity consumption, washing time and water intake. Thus, in this paper, fuzzy logic system is used to determine the washing time by considering four different inputs, which are type of fabric, type of dirt, dirtiness of fabric and weight of load. The possible rules from the input variables are developed by combining all the variables using fuzzy IF-THEN rule. Referring to the Mamdani inference engine, a minimum membership function from input parts is truncated to the output for each of the rules. Next, the maximum membership function from the output is aggregated and the washing time can be calculated by using centroid method. The comparison is done by comparing the washing time of four input variables with three input variables.
Abusive comment identification on Indonesian social media data using hybrid deep learning Tiara Intana Sari; Zalfa Natania Ardilla; Nur Hayatin; Ruhaila Maskat
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.pp895-904

Abstract

Half of the entire social media users in Indonesia has experienced cyberbullying. Cyberbullying is one of the treatments received as an attack with abusive words. An abusive word is a word or phrase that contained harassment and is expressed be it spoken or in the form of text. This is a serious problem that must be controlled because the act has an impact on the victim's psychology and causes trauma resulting in depression. This study proposed to identify abusive comments from social media in Indonesian language using a deep learning approach. The architecture used is a hybrid model, a combination between recurrent neural network (RNN) and long short-term memory (LSTM). RNN can map the input sequences to fixed-size vectors on hidden vector components and LSTM implemented to overcome gradient vector growth components that have the potential to exist in RNN. The steps carried out include preprocessing, modelling, implementation, and evaluation. The dataset used is indonesian abusive and hate speech from Twitter data. The evaluation result showed that the model proposed produced an f-measure value of 94% with an increase in accuracy of 23%.
A real-time quantum-conscious multimodal option mining framework using deep learning Jamuna S. Murthy; Siddesh Gaddadevara Matt; Sri Krishna H. Venkatesh; Kedarnath R. Gubbi
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.pp1019-1025

Abstract

Option mining is an arising yet testing artificial intelligence function. It aims at finding the emotional states and enthusiastic substitutes of expounders associated with a discussion based on their suppositions, which are conveyed by various techniques of data. But there exist an abundance of intra and inter expression collaboration data that influences the feelings ofexpounders in a perplexing and dynamic manner. Step by step instructions to precisely and completely model convoluted associations is the critical issue of the field. To pervade this break, an innovative and extensive system for multimodal option mining framework called a “quantum-conscious multimodal option mining framework (QMF)”, is introduced. This uses numerical ceremoniousness of quantum hypothesis and a long transientmemory organization. QMF system comprise of a multiple-modal choice combination method roused by quantum obstruction hypothesis to catch the co- operations inside every expression and a solid feeble impact model motivated by quantum multimodal (QM) hypothesis to demonstrate the communications between nearby expressions. Broad examinations are led on two generally utilized conversational assessment datasets: the multimodal emotional lines dataset (MELD) and interactive emotional dyadic motion capture (IEMOCAP) datasets. The exploratory outcomes manifest that our methodology fundamentally outflanks a broadscope of guidelines and best in class models.
A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine Baydaa Sulaiman Bahnam; Suhair Abd Dawwod
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.pp1164-1174

Abstract

One of the most dangerous health diseases affecting the world's population is diabetes mellitus (DM), and its diagnosis is the key to its treatment. Several methods have been implemented to diagnose diabetes patients. In this work, a hybrid model which combines of coyote optimization algorithm (COA) and least squares support vector machine (LS-SVM) is proposed to classify of Type-II-DM patients. LS-SVM classifier is applied for classification process but it's very sensitive when its parameter values are changed. To overcome this problem, COA algorithm is implemented to optimize parameters of the LS-SVM classifier. This is the goal of the proposed model called the COA-LS-SVM. The proposed model is implemented and evaluated using pima Indians diabetes dataset (PIDD). Also, it's compared with several classification algorithms that were implemented on the same PIDD. The experimental results demonstrated the effectiveness of the proposed model and its superiority over other algorithms, as it could accomplish an average classification accuracy of 98.811%.
Artificial intelligence in a communication system for air traffic controllers' emergency training Youssef Mnaoui; Aouatif Najoua; Hassan Ouajji
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.pp986-994

Abstract

In the last few years, there has been a lot of research into the use of machine learning for speech recognition applications. However, applications to develop and evaluate air traffic controllers' communication skills in emergency situations have not been addressed so far. In this study, we proposed a new automatic speech recognition system using two architectures: The first architecture uses convolutional neural networks and gave satisfactory results: 96% accuracy and 3% error rate on the training dataset. The second architecture uses recurrent neural networks and gave very good results in terms of sequence prediction: 99% accuracy and ???? −7% error rate on the training dataset. Our intelligent communication system (ICS) is used to evaluate aeronautical phraseology and to calculate the response time of air traffic controllers during their emergency management. The study was conducted at International Civil Aviation Academy, with third-year air traffic control engineering students. The results of the trainees' performance prove the effectiveness of the system. The instructors also appreciated the instantaneous and objective feedback.
Binary spider monkey algorithm approach for optimal siting of the phasor measurement unit for power system state estimation Suresh Babu Palepu; Manubolu Damodar Reddy
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.pp1033-1040

Abstract

The phasor measurement unit (PMU) is an essential measuring device in current power systems. The advantage seems to be that the measuring system could simultaneously give voltages and currents phasor readings from widely dispersed locations in the electric power grid for state estimation and fault detection. Simulations and field experiences recommend that PMUs can reform the manner power systems are monitored and controlled. However, it is felt that expenses will limit the number of PMUs that will be put into any power system. Here, PMU placement is done using a binary spider monkey optimization (BSMO) technique that uses BSMO by simulating spider monkeys’ foraging behavior. Spider monkeys have been classified as animals with a fission-fusion social structure. Animals that follow fission-fusion social systems divide into big and tiny groups, and vice versa, in response to food shortage or availability. The method under development produced the optimum placement of PMUs while keeping the network fully observable under various contingencies. In the study published in IEEE14, IEEE24, IEEE30, IEEE39, IEEE57, and IEEE118, the proposed technique was found to reduce the number of PMUs needed.