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IAES International Journal of Artificial Intelligence (IJ-AI)

IJAI
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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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Sensitivity analysis of a species conserving genetic algorithm's parameters for addressing the niche radius problem Michael Scott Brown
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Niche Genetic Algorithms (NGA) are a special category of Genetic Algorithms (GA) that solve problems with multiple optima. These algorithms preserve genetic diversity and prevent the GA from converging on a single optima. Many NGAs suffer from the Niche Radius Problem (NRP), which is the problem of correctly setting a radius parameter for optimal results. While the selection of the radius value has been widely researched, the effects of other GA parameters on genetic diversity is not well known. This research is a parameter sensitivity analysis on the other parameters in a GA, namely mutation rate, number of individuals and number of generations.

Prediction of cutting and feed forces for conventional milling process using adaptive neuro fuzzy inference system (ANFIS) Kanhu Charan Nayak; Rajesh Kumar Tripathy; Sudha Rani Panda; Shiba Narayan Sahoo
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 1: March 2014
Publisher : Institute of Advanced Engineering and Science

Due to the extensive use of highly automated machine tools in the industry, the manufacturing requires reliable models for the prediction of output performance of machining processes. The prediction of cutting forces plays an important role in the manufacturing industry. The focus of this paper is to develop a reliable method to predict cutting forces (force in X-direction and force in Z-direction) for milling process during conventional machining of mild steel. This paper implements an adoptive Neuro-fuzzy interface system (ANFIS) to actualize an efficient model for prediction of cutting forces during conventional milling. A set of three input machining parameters like speed, feed and depth of cut, which has a major impact on the cutting forces was chosen as input to represent the machining condition. Our result confirms that ANFIS model with Gaussian member function is a better predictive tool for prediction of milling forces with minimum average test error.

Predictive analytics of university student intake using supervised methods Muhammad Yunus Iqbal Basheer; Sofianita Mutalib; Nurzeatul Hamimah Abdul Hamid; Shuzlina Abdul-Rahman; Ariff Md Ab Malik
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Predictive analytics extract important factors and patterns from historical data to predict future outcomes. This paper presents predictive analytics of university student intake using supervised methods. Every year, universities face a lot of academic offer rejection by the applicants. Hence, this research aims to predict student acceptance and rejection towards academic offer given by a university using supervised methods subject to past student intake data. To solve this problem, a lot of past studies had been reviewed starting from nineties era till now. From the analysis, two algorithms had been selected namely Decision Tree and k Nearest Neighbor. The dataset of past student intake was obtained with fifteen attributes, which are applicants’ gender, applicants studied stream during Sijil Peperiksaan Malaysia (SPM), university campuses, applicants’ hometown, disability, campus visit, course choice order in application form, applicant’s six SPM subjects result, orphan and status of acceptance. Several experiments were implemented to find the best model to predict the student’s offer acceptance by evaluating the model accuracy. Both models yield best accuracy at 66 percent with the selected attributes. This research gives a huge impact in selecting which applicants is suitable to be offered as well as adapting the university’s academic offering process in much intelligence way in the future.

Offline Signature Verification and Forgery Detection Based on Computer Vision and Fuzzy Logic Gautam S. Prakash; Shanu Sharma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 3, No 4: December 2014
Publisher : Institute of Advanced Engineering and Science

Automated signature verification and forgery detection has many applications in the field of Bank-cheque processing,document authentication, ATM access etc. Handwritten signatures have proved to be important in authenticating a person's identity, who is signing the document. In this paper a Fuzzy Logic and Artificial Neural Network Based Off-line Signature Verification and Forgery Detection System is presented. As there are unique and important variations in the feature elements of each signature, so in order to match a particular signature with the database, the structural parameters of the signatures along with the local variations in the signature characteristics are used. These characteristics have been used to train the artificial neural network. The system uses the features extracted from the signatures such as centroid, height – width ratio, total area, Ist and IInd order derivatives, quadrant areas etc. After the verification of the signature the angle features are used in fuzzy logic based system for forgery detection.

Hourly wind speed forecasting based on support vector machine and artificial neural networks Soukaina Barhmi; Omkalthoume El Fatni
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Wind speed is the main component of wind power. Therefore, wind speed forecasting is of big importance due to its uses. It permits to plan the dispatch, determine the hours of storage needed, the amount of energy stored that should be used and avoid the big fluctuations in the electrical grid caused by the nature of the renewable energy resources. In this paper, we propose four hybrid models based on Support Vector Machine (SVM) and Artificial Neural Networks (ANNs) or just Neural Networks (NN) for wind speed forecasting. Using the Ordinary Least Squares (OLS) analysis for selecting the parameters more influencing wind speed. Then, a Support Vector Machine and Artificial Neural Networks models are tuned by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance of these models is evaluated using three statistical indicators: the Mean Square Error (MSE), Mean Error (ME) and Mean Absolute Error (MAE). The results show a better performance of the neural model compared to the support vector machine.

Artificial Intelligence a Threat Abhedya Saini
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 5, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Research in AI has built upon the tools and techniques of many different disciplines.Study in the artificial intelligence has given rise to rapidly growing technology known as expert system. Rapid development in this field made human more dependent on this technology. More advancement will lead to side effects of that technology because after a certain point, everything is harmful.

Towards a semantic web of things framework Nadim Ismai; El Ghayam Yassine; Abdelalim Sadiq
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Information and communication teFchnologies (ICT) know a significant development especially in terms of hardware miniaturization, cost reduction and energy consumption optimization. This advancement enables the interconnection of a large number of physical objects namely using the Internet, forming what is called the Internet of Things (IoT). The IoT provides the opportunity to interact with these objects through sensors, actuators and smart applications which may help users in several areas such as transport, logistics, health care, agriculture, etc. However, building the IoT requires a strong interoperability between thousands of heterogeneous devices and services. In this context, the SWoT (Semantic Web of Things) uses semantic Web technologies to enrich these devices and services with semantic annotations which enables the semantic interoperability. However, the development of SWOT-based systems on a large scale faces many challenges especially due to the large number of devices and services, their geographical distribution as well as their mobility. These challenges-which may affect the system performance as a whole-require innovative industry and research efforts. The current paper proposes a SWoT framework architecture that take into account the main SWoT challenges.

A New DG Allocation Approach Based on Biogeography-Based Optimization with Considering Fuzzy Load Uncertainty Mohammad Sedaghat; Esmaeel Rokrok; Mohammad Bakhshipour
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 3: September 2015
Publisher : Institute of Advanced Engineering and Science

A new distributed generation placement method based on biogeography-based optimization (BBO) is investigated in this paper. A significant novelty of this study lies in considering fuzzy load uncertainty. For this purpose a fuzzy backward- forward sweep load flow is proposed. The main objectives of this study is minimizing power losses and improving voltage profile. A comparative study between optimal location and sizing under typical load condition and fuzzy load uncertainty is presented. To verify the efficiency of proposed BBO method, it is conducted on IEEE 33 bus distribution system, also a comparative study between proposed BBO approach and particle swarm optimization (PSO), Technical-learning based optimization (TLBO), Artificial bee colony (ABC), Imperialist competitive algorithm (ICA) is investigated. The simulation results show the excellent and superior performance of proposed BBO approach in comparison with the other intelligent methods.

Deep Machine Learning and Neural Networks: An Overview Chandrahas Mishra; D. L. Gupta
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 6, No 2: June 2017
Publisher : Institute of Advanced Engineering and Science

Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and picture. Deep learning is as of now a greatly dynamic examination territory in machine learning and example acknowledgment society. It has increased colossal triumphs in an expansive zone of utilizations, for example, speech recognition, computer vision and natural language processing and numerous industry item. Neural network is used to implement the machine learning or to design intelligent machines. In this paper brief introduction to all machine learning paradigm and application area of deep machine learning and different types of neural networks with applications is discussed.

Predictive Modelling and Optimization of Power Plant Nitrogen Oxides Emission Ilamathi P; V. Selladurai; K. Balamurugan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 1, No 1: March 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (396.994 KB)

A predictive modelling of nitrogen oxides emission from a 210 MW coal fired thermal power plant with combustion parameter optimization is proposed. The oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature and nozzle tilt were studied. The parametric field experiment data were used to build artificial neural network (ANN). The coal combustion parameters were used as inputs and nitrogen oxides as output of the model. The predicted values of the ANN model for full load condition were verified with the actual values. The optimum level of input operating conditions for low nitrogen oxides emission was determined by simulated annealing (SA) approach. The result indicates that the combined approach could be used for reducing nitrogen oxides emission.DOI: http://dx.doi.org/10.11591/ij-ai.v1i1.286

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Home Page

OAI Link

Editorial Team

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Reviewer

Google Scholar

Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

Phone
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Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA

Abstract

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Imam Much Ibnu Subroto

Contact Email imam@unissula.ac.id

Phone -

Journal Mail Official ijai@iaesjournal.com

Editorial Address -

Location Kota yogyakarta, Daerah istimewa yogyakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Imam Much Ibnu Subroto

Contact Email
imam@unissula.ac.id

Phone
-

Journal Mail Official
ijai@iaesjournal.com

Editorial Address
-

Location
Kota yogyakarta,

Daerah istimewa yogyakarta

INDONESIA