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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 15 Documents
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Search results for , issue "Vol 8, No 3: September 2019" : 15 Documents clear
Computational intelligence based lossless regeneration (CILR) of blocked gingivitis intraoral image transportation Anirban Bhowmik; Joydeep Dey; Arindam Sarkar; Sunil Karforma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (783.751 KB) | DOI: 10.11591/ijai.v8.i3.pp197-204

Abstract

This paper presented that an intraoral image has been wrapped during wireless transportation with an encryption tool with an added essence of lossless regeneration property. Threshold based cryptographic transportation has provided the construction of reliable and robust medical data communication system. The accumulation of threshold shares only would result to the formation of the intraoral gingivitis image at the receivers’ end. The proposed technique dealt with the generation of n number of partial shares by creating a unique frame structure by the dentist / physician. Additional feature has been proposed on the computational lossless transportation.The existing techniques cause a high computational complexity. The proposed technique ensured the lossless regeneration property while blocked gingivitis image sharing. Filling of bits have been incorporated to ensure the static sized homogeneous blocks of intraoral gingivitis image. A graphical masking method had been deployed, followed by successive decryption procedure on minimum threshold shares that ensure lossless data regeneration. This can guide the dental treatment with enhanced accuracy. Different types of statistical testing like entropy analysis and histogram analysis confirms the exhibition of authenticity, confidentiality, and integrity of our proposed technique.
Rice grain classification using multi-class support vector machine (SVM) Shafaf Ibrahim; Nurul Amirah Zulkifli; Nurbaity Sabri; Anis Amilah Shari; Mohd Rahmat Mohd Noordin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (489.854 KB) | DOI: 10.11591/ijai.v8.i3.pp215-220

Abstract

Presently, the demands for rice are increasing. This will affects the need for producing and sorting rice grain in faster and exceed the normal requirement. However, the manual rice classification using naked eyes are not very accurate and only professionals are able to do it. Machine learning is found to be a suitable technique for rice classification in producing an accurate result and faster solution. Thus, a study on the classification of rice grain using an image processing technique is presented. The rice grain image went through the pre-processing process which includes the grayscale and binary conversion, and segmentation before the feature extraction process. Four attributes of shape descriptor which are area, perimeter, major axis length, and minor axis length and three attributes of color descriptor which are hue, saturation and value were extracted from each rice grain image. In another note, a Multi-class Support Vector Machine (SVM) is used to classify the three types of rice grain which are basmathi, ponni and brown rice. The performance of the proposed study is evaluated to 90 testing images which returned 92.22% of classification accuracy. The study is expected to assist the Agrotechnology industry in automatic classification of rice grain in the future.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (460.88 KB) | DOI: 10.11591/ijai.v8.i3.pp286-291

Abstract

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.
Review of anomalous sound event detection approaches Amirul Sadikin Md Affendi; Marina Yusoff
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (352.382 KB) | DOI: 10.11591/ijai.v8.i3.pp264-269

Abstract

This paper presents a review of anomalous sound event detection (SED) approaches. SED is becoming more applicable for real-world appliactaions such as security, fire determination or olther emergency alarms. Despite many research outcome previously, further research is required to reduce false positives and improve accurracy. SED approaches are comprehensively organized by methods covering system pipeline components of acoustic descriptors, classification engine, and decision finalization method. The review compares multiple approaches that is applied on a specific dataset. Security relies on anomalous events in order to prevent it one must find these anomalous events. Audio surveillance has become more efficient as that artificial intelligence has stepped up the game. Autonomous SED could be used for early detection and prevention. It is found that the state of the art method viable used in SED using features of log-mel energies in convolutional recurrent neural network (CRNN) with long short term memory (LSTM) with a verification step of thresholding has obtained 93.1% F1 score and 0.1307 ER. It is found that feature extraction of log mel energies are highly reliable method showing promising results on multiple experiments.
Performance comparison of various probability gate assisted binary lightning search algorithm Md Mainul Islam; Hussain Shareef; Mahmood Nagrial; Jamal Rizk; Ali Hellany; Saiful Nizam Khalid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 8, No 3: September 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.858 KB) | DOI: 10.11591/ijai.v8.i3.pp299-306

Abstract

Recently, many new nature-inspired optimization algorithms have been introduced to further enhance the computational intelligence optimization algorithms. Among them, lightning search algorithm (LSA) is a recent heuristic optimization method for resolving continuous problems. It mimics the natural phenomenon of lightning to find out the global optimal solution around the search space. In this paper, a suitable technique to formulate binary version of lightning search algorithm (BLSA) is presented. Three common probability transfer functions, namely, logistic sigmoid, tangent hyperbolic sigmoid and quantum bit rotating gate are investigated to be utilized in the original LSA. The performances of three transfer functions based BLSA is evaluated using various standard functions with different features and the results are compared with other four famous heuristic optimization techniques. The comparative study clearly reveals that tangent hyperbolic transfer function is the most suitable function that can be utilized in the binary version of LSA.

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