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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 30 Documents
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Search results for , issue "Vol 10, No 1: March 2021" : 30 Documents clear
Spike neuron optimization using deep reinforcement learning Tan Szi Hui; Mohamad Khairi Ishak
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp175-183

Abstract

Deep reinforcement learning (DRL) which involved reinforcement learning and artificial neural network allows agents to take the best possible actions to achieve goals. Spiking Neural Network (SNN) faced difficulty in training due to the non-differentiable spike function of spike neuron. In order to overcome the difficulty, Deep Q network (DQN) and Deep Q learning with normalized advantage function (NAF) are proposed to interact with a custom environment. DQN is applied for discrete action space whereas NAF is implemented for continuous action space. The model is trained and tested to validate its performance in order to balance the firing rate of excitatory and inhibitory population of spike neuron by using both algorithms. Training results showed both agents able to explore in the custom environment with OpenAI Gym framework. The trained model for both algorithms capable to balance the firing rate of excitatory and inhibitory of the spike neuron. NAF achieved 0.80% of the average percentage error of rate of difference between target and actual neuron rate whereas DQN obtained 0.96%. NAF attained the goal faster than DQN with only 3 steps taken for actual output neuron rate to meet with or close to target neuron firing rate.
Vehicle detection and tracking for traffic management Mallikarjun Anandhalli; Vishwanth P. Baligar; Pavana Baligar; Pooja Deepsir; Mithali Iti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp66-73

Abstract

The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.
Virtual machine migration in MEC based artificial intelligence technique Ali OUACHA; Mohamed EL Ghmary
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp244-252

Abstract

The whole world is inundated with smaller devices equipped with wireless communication interfaces. At the same time, the amount of data generated by these devices is becoming more important. The smaller size of these devices has the disadvantage of being short of processing and storage resources (memory, processes, energy,...), especially when it needs to process larger amounts of data. In order to overcome this weakness and process massive data, devices must help each other. A low-resource node can delegate the execution of a set of computionly heavy tasks to another machine in the network to process them for it. The machine with sufficient computational resources must also deposit the appropriate environment represented by the adapted virtual machine. Thus, in this paper, in order to migrate the virtual machine to an edge server in a mobile edge computing environment, we have proposed an approach based on artificial intelligence. More specifically, the main idea of this paper is to cut a virtual machine into several small pieces and then send them to an appropriate target node (Edge Server) using the ant colony algorithm. In order to test and prove the effectiveness of our approach, several simulations are made by NS3. The obtained results show that our approach is well adapted to mobile environments.
Expert system for heart disease based on electrocardiogram data using certainty factor with multiple rule Sumiati Sumiati; Hoga Saragih; Titik Abdul Rahman; Agung Triayudi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp43-50

Abstract

Limited public health services in remote areas, where the lack of transportation infrastructure, facilities, communication facilities and minimal medical personnel, especially for areas with underdeveloped, foremost, and regular (3T) status. The limitation of medical personnel is one of the factors for the high mortality rate of heart disease. On the other hand, the development of information technology, especially in the field of computing, is very fast in the era of the industrial revolution 4.0, but not yet used optimally, especially in the health sector. This study aims to develop a system or software that can replace a doctor for the process of identifying heart defects based on an expert system. Expert system developed with the certainty factor with multiple rule approach. System testing is carried out from the results of the system validity with experts, so that the system test results produce a certainty factor value for a normal heart of 0.95 and an accuracy level of 95%, while the certainty factor (CF) value for an abnormal heart is 0.99 and produces an accuracy rate of 99%.
Recent development of smart traffic lights A’isya Nur Aulia Yusuf; Ajib Setyo Arifin; Fitri Yuli Zulkifli
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp224-233

Abstract

Increased traffic flow causes congestion, especially in large cities. Even though congestion is not unusual, traffic jams still result in very high economic and social losses. Several factors cause congestion, one of which is traffic lights. Therefore, a mechanism is needed so that traffic lights can intelligently and adaptively manage signal time allocation according to traffic flow conditions. A traffic light with this type of mechanism is known as a smart traffic light. Smart traffic light cycle settings can be grouped based on the traffic density, scenarios for emergency vehicles, and the interests of pedestrians. This paper analyzes the methods and technologies used in the development of smart traffic light technology from the perspective of these three situations as well as the development of smart traffic light technology in the future.
Enhancement of energy consumption estimation for electric vehicles by using machine learning Adnane Cabani; Peiwen Zhang; Redouane Khemmar; Jin Xu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp215-223

Abstract

Three main classes are considered of significant influence factors when predicting the energy consumption rate of electric vehicles (EV): environment, driver behaviour, and vehicle. These classes take into account constant or variable parameters which influences the energy consumption of the EV. In this paper, we develop a new model taking into account the three classes as well as the interaction between them in order to improve the quality of EV energy consumption. The model depends on a new approach based on machine learning and especially k-NN algorithm in order to estimate the EV energy consumption. Following a lazy learning paradigm, this approach allows better estimation performance. The advantage of our proposal, in regards to mathematical approach, is taking into account the real situation of the ecosystem on the basis of historical data. In fact, the behavior of the driver (driving style, heating usage, air conditioner usage, battery state, etc.) impacts directly the EV energy consumption. The obtained results show that we can reach up to 96.5% of accuracy about the estimated of energy-consumption. The proposed method is used in order to find the optimal path between two points (departure-destination) in terms of energy consumption.
Analysis of spammers’ behavior on a live streaming chat Sawita Yousukkee; Nawaporn Wisitpongphan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp139-150

Abstract

Live streaming is becoming a popular channel for advertising and marketing. An advertising company can use this feature to broadcast and reach a large number of customers. YouTube is one of the streaming media with an extreme growth rate and a large number of viewers. Thus, it has become a primary target of spammers and attackers. Understanding the behavior of users on live chat may reduce the moderator’s time in identifying and preventing spammers from disturbing other users. In this paper, we analyzed YouTube live streaming comments in order to understand spammers’ behavior. Seven user’s behavior features and message characteristic features were comprehensively analyzed. According to our findings, features that performed best in terms of run time and classification efficiency is the relevant score together with the time spent in live chat and the number of messages per user. The accuracy is as high as 66.22 percent. In addition, the most suitable technique for real-time classification is a decision tree.
Ensemble learning model for Wifi indoor positioning systems Doan Tinh Pham; Ta Thi Ngoc Mai
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp200-206

Abstract

WiFi indoor positioning researches have received much attention from researchers recently. In this research, we focus on studying the performance of indoor positioning systems that utilize our new proposed ensemble machine learning model. Our new ensemble learning model uses several models for normal data training and position prediction, then it uses the verification data together with its' prediction errors from trained models as the input data to train an intermediate classification model to classify which set of Wifi received signal strength indicator (RSSI) is the best match for each position prediction model. The experimental result shows that our proposed ensemble model outperforms other compared models.
Massively scalable density based clustering (DBSCAN) on the HPCC systems big data platform Yatish H. R.; Shubham Milind Phal; Tanmay Sanjay Hukkeri; Lili Xu; Shobha G; Jyoti Shetty; Arjuna Chala
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp207-214

Abstract

Dealing with large samples of unlabeled data is a key challenge in today’s world, especially in applications such as traffic pattern analysis and disaster management. DBSCAN, or density based spatial clustering of applications with noise, is a well-known density-based clustering algorithm. Its key strengths lie in its capability to detect outliers and handle arbitrarily shaped clusters. However, the algorithm, being fundamentally sequential in nature, proves expensive and time consuming when operated on extensively large data chunks. This paper thus presents a novel implementation of a parallel and distributed DBSCAN algorithm on the HPCC Systems platform. The algorithm seeks to fully parallelize the algorithm implementation by making use of HPCC Systems optimal distributed architecture and performing a tree-based union to merge local clusters. The proposed approach* was tested both on synthetic as well as standard datasets (MFCCs Data Set) and found to be completely accurate. Additionally, when compared against a single node setup, a significant decrease in computation time was observed with no impact to accuracy. The parallelized algorithm performed eight times better for higher number of data points and takes exponentially lesser time as the number of data points increases.
A simulation energy management system of a multi-source renewable energy based on multi agent system Aoukach Basma; Oukarfi Benyounes
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp191-199

Abstract

The intermittent nature of renewable energies sources makes their control difficult. One of the solutions to overcome this handicap is to promote hybridization (multi-source system). To ensure continuity of service, a storage system must be coupled to the system. To do so, artificial intelligence based models are developed to respond optimally to the dilemma of energy supply and demand. These models allow the management of the energy flow between the sources (photovoltaic, wind, battery, super capacitor, and generator) and the variable loads by controlling electronic switches according to the availability of the sources. The artificial intelligence algorithm used in this study is multi agent system (MAS). The simulation results and validation tests shows the effectivenes of the proposed approach.

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