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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 40 Documents
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Search results for , issue "Vol 11, No 2: June 2022" : 40 Documents clear
State of charge estimation of lithium-ion batteries using adaptive neuro fuzzy inference system Imane Chaoufi; Othmane Abdelkhalek; Brahim Gasbaoui
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.pp473-484

Abstract

A battery’s state of charge (SOC) is used to assess its residual capacity. It is a very important parameter for the control of the electric vehicle (EV). The objective of this paper is to estimate the SOC of a lithium-ion battery (LIB) using an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) because SOC of a battery must be estimated from measurable battery parameters such as current, voltage or temperature. Two intelligent SOC estimation methods are compared according to their suitability and accuracy. ANN estimation is more precise and perfectly represents the experimental data.
Fake news detection using naïve Bayes and long short term memory algorithms Sarra Senhadji; Rania Azad San Ahmed
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.pp746-752

Abstract

Information and communication technologies have revolutionized the numerical world by offering the freedom to publish and share all types of information. Unfortunately, not all information circulated on the internet is accurate, which can have serious consequences, including misleading readers. Detecting false news is a complicated task to overcome. Massive studies focus on using machine and deep learning techniques in an attempt to classify the news as authentic or not. The goal of this research is an attempt to glance and evaluate how naïve bayes (NB) and long short-term memory (LSTM) classifiers can be used to positively identify fake news. The outcomes of this experiment reveal that LSTM achieves an accuracy of 92 percent over naive bayes. Moreover, the findings of the proposed approach’s results outperform the related work results.
Intrusion prevention system using convolutional neural network for wireless sensor network Pankaj Ramchandra Chandre; Parikshit Mahalle; Gitanjali Shinde
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.pp504-515

Abstract

Now-a-days, there is exponential growth in the field of wireless sensor network. In wireless sensor networks (WSN’s), most of communication happen through wireless media hence probability of attacks increases drastically. With the help of intrusion prevention system, we can classify user activities into two categories, normal and suspicious activity. There is need to design effective intrusion prevention system by exploring deep learning for WSN. This research aims to deal with proposing algorithms and techniques for intrusion prevention system using deep packet inspection based on deep learning. In this, we have proposed deep learning model using convolutional neural network. The proposed model includes two steps, intrusion detection and intrusion prevention. The proposed model learns useful feature representations from large amount of labeled data and then classifies them. In this work, convolutional neural network is used to prevent intrusion for WSN. To evaluate and check the effectiveness of the proposed system, the wireless sensor network dataset (WSNDS) dataset is used and the tests are performed. The test results show that proposed system has an accuracy of 97% and works better than existing system. The proposed work can be used as future benchmark for the deep learning and intrusion prevention research communities.
COVID-19 epidemic: analysis and prediction Santosini Bhutia; Bichitrananda Patra; Mitrabinda Ray
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.pp736-745

Abstract

“Novel Coronavirus”, commonly known as COVID-19 has spread nearly to the entire world. The number of impacted cases and deaths has increased significantly in each country, posing a challenge for the world’s health organizations. The goal of this paper was to better comprehend and analyze the growth of the disease in India, including confirmed, recovered, fatalities, and active cases of COVID-19. Data analysis affects an organization’s decision-making process with interactive visual representation. The proposed model was an ensemble model that was built using linear regression, polynomial regression, and support vector machine (SVM) regression models. The model predicted the number of confirmed cases from 30 th May 2021 to 15 th June 2021 based on the data available from 22 January 2020 to 29 May 2021 and improved accuracy was obtained when compared with the actual data. Forecasting the confirmed cases might assist health organizations in planning medical facilities. Following that, an appropriate machine leraning (ML) model must be found that can predict the number of new cases in the future.
DistractNet: a deep convolutional neural network architecture for distracted driver classification Ismail Nasri; Mohammed Karrouchi; Hajar Snoussi; Kamal Kassmi; Abdelhafid Messaoudi
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.pp494-503

Abstract

Distracted driving has been considered one of the reasons for traffic accidents. The american national highway traffic safety administration (NHTSA) defines distracted driving as any activity that takes attention away from driving, such as doing makeup, texting, calling, and reaching behind. Most deaths, physical injuries, and economic losses could have been prevented if the distracted driver is alerted on time. This paper has proposed a new convolutional neural network (CNN) called DistractNet to detect drivers' distractions. The proposed model was trained and tested by state farm distracted driver detection image datasets available at Kaggle that contains images of drivers in the most common activities performed, which lead to distraction while driving divided into ten classes. Also, we have studied the performances of the proposed CNN model based on accuracy, training time, and model size. The performance of the proposed model was compared with four pre-trained networks such as ResNet-50, GoogLeNet, InceptionV3, and AlexNet using transfer learning techniques. The obtained experimental results show that the developed model-based CNN can achieve an overage accuracy of more than 99.32% with 93 min of training time and 7.99 MB of size. The extracted model can classify driver states into ten different classes with the predicted label and probability % for each class.
Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions Alhadi Bustamam; Mohamad Irlin Sunggawa; Titin Siswantining
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.pp773-786

Abstract

Protein interactions play an essential role in the study of how an organism can be infected with a disease and also its effects. One of the challenges in computational methods in the prediction of protein-protein interactions is how to represent a sequence of amino acids in a vector so that it can be used in machine learning to create a model that can predict whether or not an interaction occurs in a protein pair. This paper examined the qualitative feature encoding methods of amino acid sequence, namely, multivariate mutual information (MMI), and the quantitative feature encoding methods, namely, autocorrelation. We develop the new design for MMI and autocorrelation feature encoding methods which give better results than the previous research. There are four ways to build the MMI method and six ways to build the autocorrelation method that we tested. We also built four types of MMI-autocorrelation (mixed) method and look for the best form of each type of MMI, autocorrelation, and mixed-method. We combine these feature encoding methods with support vector machine (SVM) as machine learning methods. We also test the encoding methods we propose to several machine learning classifier methods, such as random forest (RF), k-nearest neighbor (KNN), and gradient boosting.
Evolutionary model to guarantee quality of service for tactical worldwide interoperability for microwave access networks Ravishankar Huchappa; Kiran Kumari Patil
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.pp687-698

Abstract

The smart phone industries evolution had made the growth in wireless communication. The increase in social media usage led to huge increase of network traffic as sharing of data for multimedia like video conferencing, and voice over internet protocol (VoIP). Majority of services like this requires network resources and real-time strict quality of service (QoS). However high cost of network deployment is included. All the major service providers are currently being adopted to the WiMAX 802.16 network. Therefore, the worldwide interoperability for microwave access (WiMAX) network must have different supply policies and QoS for various applications. The implementation of these QoS policies was not provided by WiMAX for various needs of application. Recently development of various scheduling mechanism for QoS provisioning has been made. However, users improper synchronization made these models inefficient. To address this, uplink scheduling with feedback is considered to provision QoS. However, it induces delay in accessing slots, as result the bandwidth is wasted. To utilize bandwidth efficiently evolutionary computing is adopted by various existing model. However, these models induce computation overhead and may not be suitable for provisioning real-time services. The evolutionary computing model is used to compute ideal threshold.
A new pedestrian recognition system based on edge detection and different census transform features under weather conditions Mohammed Razzok; Abdelmajid Badri; Ilham El Mourabit; Yassine Ruichek; Aïcha Sahel
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.pp582-592

Abstract

Pedestrian detection has so far achieved great success in normal illumination, while pedestrians captured in extreme weather are often ignored. This paper investigates the importance of studying the effects of weather conditions on the recognition task, such as blurring and low contrast. Many image restoration techniques have recently been proposed, but are still insufficient to remove weather effects from images. We present our strong new pedestrian recognition system against climate situations, which is based on locating contours cues by applying multiple edge filters and extracting multiple features from images such as census transform (CT), modified census transform (MCT), and local gradient pattern (LGP) without performing any image restoration algorithm. The next stage involves finding the most discriminative characteristics using feature selection (FS) techniques. Finally, we use the final feature vector as an input to a radial basis function-based support vector machine classifier (RbfSVM) for pedestrian recognition. Experiments are performed on the daimler pedestrian classification benchmark dataset. Results show that the area under the curve (AUC) and the detection rate of our model are less affected by weather conditions compared to other common models like histogram of oriented gradients (HOG) and gabor filter bank (GFB) detectors.
Modeling of artificial neural networks for silicon prediction in the cast iron production process Wandercleiton Cardoso; Renzo di Felice; Bruna Nunes dos Santos; Arthur Nascimento Schitine; Thiago Augusto Pires Machado; André Gustavo de Sousa Galdino; Pedro Vitor Morbach Dixini
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.pp530-538

Abstract

The main way to produce cast iron is in the blast furnace. In the production of hot metal, the control of silicon is important. Alumina and silica react chemically with limestone and dolomite to form blast furnace slag. In this work, 12 artificial neural networks (ANNs) were modeled with different numbers of neurons in each hidden layer. The number of neurons varied between 10 and 200 neurons. ANNs were used to predict the silicon content of hot metal produced. The ANN with 30 neurons showed the best performance. In the test phase, the mathematical correlation was 97.5% and the mean square error (MSE) was 0.0006, and in the cross-validation phase, the mathematical correlation was 95.5% while the MSE was 0.00035.
A hunger game search algorithm for economic load dispatch Widi Aribowo; Supari Muslim; Bambang Suprianto
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.pp632-640

Abstract

This work proposes a new approach to solve the economic load dispatch (ELD) issue in power systems by metaheuristic algorithms inspired by natural life. The problem to be resolved is to optimize the power system network with various constraints by considering the cutting in the cost of the resulting in the transmission of the electric system. The method used in this study is the hunger games search (HGS). This method duplicates the hungerdriven activity and the animal's choice of behavior. The proposed method is to add the concept of starvation as a process structure. Adaptive weights based on the concept of hunger are designed and used to simulate the effects of hunger on each trace process. To get the performance of the proposed method, this research uses mathematical methods, particle swarm optimization (PSO), differential evolution (DE), giza pyramids construction (GPC), and sine tree-seed algorithm (STSA) as a comparison. This study uses 2 case studies. In case study 1, the proposed method has a 0.16% better cost of generation than the mathematical method. Comparison of the HGS method with the PSO method in the second case study, it was found that the HGS method was 0.018% better than the PSO. From the research, it was found that the HGS method was superior.

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