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INDONESIA
International Journal of Intelligent Systems and Applications in Engineering
Published by Ismail SARITAS
ISSN : 21476799     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Intelligent Systems and Applications in Engineering (IJISAE) is an international and interdisciplinary journal for both invited and contributed peer reviewed articles that intelligent systems and applications in engineering at all levels. The journal publishes a broad range of papers covering theory and practice in order to facilitate future efforts of individuals and groups involved in the field. IJISAE, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems in engineering. Its coverage also includes papers on intelligent systems applications in areas such as nanotechnology, renewable energy, medicine engineering, Aeronautics and Astronautics, mechatronics, industrial manufacturing, bioengineering, agriculture, services, intelligence based automation and appliances, medical robots and robotic rehabilitations, space exploration and etc.
Arjuna Subject : -
Articles 200 Documents
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Determining the Carrot Volume via Radius and Length Using ANN Örnek, Mustafa Nevzat; Kahramanli, Humar
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 2 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018642081

Abstract

In this study a total of 464 carrots were taken from Kaşınhanı, where the most carrots are produces in Turkey. The length and radiuses with an interval of 5 cm and volume were measured and recorded. Three different Artificial Neural Network models: BP, LM and PUNN were designed for predicting the carrot volume. To assess the success of the system, statistical measures such as Root Mean Squared Error, Mean Absolute Error and R2 were used. The results were showed that all three methods are successful in this problem, while LM and PUNN seems bit.
Development a New Intelligent System for Monitoring Environment Information using Wireless Sensor Networks Dener, Murat
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017533897

Abstract

Wireless Sensor Networks are a new technology that has been on the agenda lately and can be applied to many areas. By using Wireless Sensor Networks, information can be gathered interactively and this information can be collectively evaluated and can be changed on the basis of information when necessary. In this work, a sensor node and a gateway node are designed and developed. With designed new nodes, a new intelligent system is developed. In the new system, Temperature, humidity, sound and water level data are perceived and monitored. This system can be used in all environments that need these four information. It is estimated that our work will benefit sensor network users. 
Resilient Image Feature Description through Evolution BOSTANCI, Erkan
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 2 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017528728

Abstract

Feature description is an important stage in many different vision algorithms. Image features detected by various detectors can be described using descriptors either with a binary or floating-point structure. This study presents the use of evolutionary algorithms, namely Genetic Algorithms (GA), in order to improve the robustness of the feature descriptors against increasing levels of photographic distortions such as noise or JPEG compression. Original feature descriptors were evolved in order to reduce the descriptor distance for the mentioned test cases. Results, tested using a statistical framework, suggest that the evolved descriptors offer better matching performance for two state-of-the-art descriptors.
A highly Reliable and Fully Automated Classification System for Sleep Apnea Detection Almazaydeh, Laiali; Elleithy, Khaled; Faezipour, Miad
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 3 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.47487

Abstract

Sleep apnea (SA) in the form of Obstructive sleep apnea (OSA) is becoming the most common respiratory disorder during sleep, which is characterized by cessations of airflow to the lungs. These cessations in breathing must last more than 10 seconds to be considered an apnea event. Apnea events may occur 5 to 30 times an hour and may occur up to four hundred times per night in those with severe SA [1]. Nowadays, polysomnography (PSG) is a standard testing procedure to diagnose OSA which includes the monitoring of the breath airflow, respiratory movement, and oxygen saturation (SpO2), body position, electroencephalography (EEG), electromyography (EMG), electrooculography (EOG), and electrocardiography (ECG). Therefore, a final diagnosis decision is obtained by means of medical examination of these recordings [2]. However, new simplified diagnostic methods and continuous screening of OSA is needed in order to have a major benefit of the treatment on OSA outcomes. In this regard, a portable monitoring system is developed to facilitate the self-administered sleep tests in familiar surroundings environment closer to the patients’ normal sleep habits. With only three data channels: tracheal breathing sounds, ECG and SpO2 signals, a patient does not need hospitalization and can be diagnosed and receive feedback at home, which eases follow-up and retesting after treatment.
Preferences, Utility and Prescriptive Decision Control in Complex Systems Pavlov, Yuri Pavlov
International Journal of Intelligent Systems and Applications in Engineering Vol 1, No 4 (2013)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The evaluation of the preferences based utility function is a goal of the human cantered control (management) design.The achievement of this goal depends on the determination and on the presentation of the requirements, characteristics and preferences of the human behaviour in the appropriate environment (management, control or administration of complex processes). The decision making theory, the utility and the probability theory are a possible approach under consideration. This paper presents an approach to evaluation of human’s preferences and their utilization in complex problems.The stochastic approximation is a possible resolution to the problem under consideration. The stochastic evaluation bases on mathematically formulated axiomatic principles and stochastic procedures. The uncertainty of the human preferences is eliminated as typically for the stochastic programming. The evaluation is preferences-oriented machine learning with restriction of the “certainty effect and probability distortion” of the utility assessment. The mathematical formulations presented here serve as basis of tools development. The utility and value evaluation leads to the development of preferences-based decision support in machine learning environments and iterative control design in complex problems.
Determination of Wind Potential of a Specific Region using Artificial Neural Networks Tasdemir, Sakir; Yaniktepe, Bulent; Guher, A.Burak
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 3 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017531433

Abstract

There is a widespread trend in alternative energy sources in todays world. Achieving energy without harming the environment has been the most important target of the countries in recent years. For this reason, it is necessary to make utmost use of natural energy sources such as wind, sun and water. Among these sources, wind energy is the most utilized. Because it was cheap and quickly return to investment it is carried out many studies in this area. However, the most important problem is the continuity when the wind energy is obtained. The first thing to do before a wind power plant is installed in a region is to calculate the wind potential of the area concerned. This process is long-term under normal conditions. Artificial Neural Networks (ANN) is one of the most frequently used methods for determining a wind power potential in a short time period. In this study, it is aimed to estimate the wind potential of a certain region within the boundaries of Osmaniye province. ANN was used to estimate the wind power potential. As a result of comparing the statistical values of the forecast values with the measured actual values, the performance of the method applied is indicated. The meteorology station at Osmaniye Korkut Ata University using data has been successfully estimated wind potential.
Dependability Assessment of the Railway Signalling Systems Based on the Stochastic Petri Nets Analysis Boudnaya, Jaouad; Mkhida, Abdelhak; Bououlid Idrissi, Badr
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.04059

Abstract

In this article, we propose a methodology to evaluate the performances of the railway signalling systems in terms of the availability. Firstly, level crossings in Morocco are presented. Secondly, a railway signalling system ERTMS level 2 modelling is proposed .The human factor and network failures are also taken into account. Finally, this system performance evaluation is proposed in every state (nominal way of functioning, degraded mode, and failure mode).
Banknote Classification Using Artificial Neural Network Approach Kaya, Esra; Yasar, Ali; Saritas, Ismail
International Journal of Intelligent Systems and Applications in Engineering Vol 4, No 1 (2016)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.55250

Abstract

In this study, clustering process has been performed using artificial neural network (ANN) approach on the pictures belonging to our dataset to determine if the banknotes are genuine or counterfeit.  Four input parameters, one hidden layer with 10 neurons and one output has been used for the ANN. All of these parameters were real-valued continuous. Data were extracted from images that were taken from genuine and forged banknote-like specimens. For digitization, an industrial camera usually used for print inspection was used. The final images have 400x 400 pixels. Due to the object lens and distance to the investigated object gray-scale pictures with a resolution of about 660 dpi were gained. Wavelet Transform tool were used to extractfeatures from images.  Four input parameters are processed in the hidden layer with 10 neurons and the output realizes the clustering process. The classification process of 1372 unit data by using ANN approach is sure to be a success as much as the actual data set. The regression results of the clustering process is considerably well. It is determined that the training regression is 0,99914, testing regression is 0,99786 and the validation regression is 0,9953, respectively. Based on the results obtained, it is seen that classification process using ANN is capable of achieving outstanding success.
An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains Kayabasi, Ahmet
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 1 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018637936

Abstract

Artificial Neural Networks (ANNs) have emerged as an important tool for classification problem. This paper presents an application of ANN model trained by artificial bee colony (ABC) optimization algorithm for classification the wheat grains into bread and durum. ABC algorithm is used to optimize the weights and biases of three-layer multilayer perceptron (MLP) based ANN. The classification is carried out through data of wheat grains (#200) acquired using image-processing techniques (IPTs). The data set includes five grain’s geometric parameters: length, width, area, perimeter and fullness. The ANN-ABC model input with the geometric parameters are trained through 170 wheat grain data and their accuracies are tested via 30 data. The ANN-ABC model numerically calculate the outputs with mean absolute error (MAE) of 0.0034 and classify the grains with accuracy of 100% for the testing process. The results of ANN-ABC model are compared with other ANN models trained by 4 different learning algorithms. These results point out that the ANN trained by ABC optimization algorithm can be successfully applied to classification of wheat grains. 
Particle Swarm Optimization Based Approach for Location Area Planning in Cellular Networks Algebary, Mays Saad
International Journal of Intelligent Systems and Applications in Engineering Vol 3, No 2 (2015)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.24975

Abstract

Location area planning problem plays an important role in cellular networks because of the trade-off caused by paging and registration signalling (i.e., location update). Compromising between the location update and the paging costs is essential in order to improve the performance of the network. The trade-off between these two factors can be optimized in such a way that the total cost of paging and location update can be minimized along with the link cost. Due to the complexity of this problem, meta-heuristic techniques are often used for analysing and solving practical sized instances. In this paper, we propose an approach to solve the LA planning problem based on the Particle Swarm Optimization (PSO) algorithm. The performance of the approach is investigated and evaluated with respect to the solution quality on a range of problem instances. Moreover, experimental work demonstrated the performance comparison in terms of different degree of mobility, paging load, call traffic load, and TRX load. The performance of the proposed approach outperform other existing meta-heuristic based approaches for the most problem instances.

Page 8 of 20 | Total Record : 200

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