Bilimsel çalışmalarda kullanılan bazı yapay zeka uygulamalarının ve trendlerinin incelenmesi

Year 2019, Volume: 10 Issue: 1, 249 - 262, 15.03.2019

Kemal Saplıoğlu Soner Uzundurukan

https://doi.org/10.24012/dumf.394591

Cited By: 4

Abstract

Çeşitli bilim alanlarındaki modelleme ve optimizasyon problemlerinin çözümünde yapay zeka algoritmalarının kullanımı gün geçtikçe artan bir trende sahiptir. Bilgisayar teknolojisindeki gelişmelerle birlikte yeni algoritmaları kullanan optimizasyon ve modelleme çalışmaları literatüre girmektedir. Bu çalışmada literatürde kullanılan bazı yapay zeka algoritmaları hakkında incelemeler yapılmış ve sunulmuştur. İncelenen algoritmalar Yapay Sinir Ağları, Bulanık Mantık, Adaptif Sinirsel Bulanık Çıkarım Sistemi, Genetik Algoritmalar, Yapay Arı Kolonisi, Karınca Kolonisi, Diferansiyel Gelişim Algoritması, Parçacık Sürüsü, Kedi Sürüsü Armoni Arama, Tabu Arama, Dağınık Arama ve Tepe Tırmanma Algoritmaları olarak dikkate alınmıştır.

Çalışmada, incelenen algoritmalar hakkında bazı istatistiki bilgiler verilmiştir. Yapay zeka algoritmalarının kullanıldığı zaman aralıkları, bu zaman aralıklarında yıllara bağlı yayın sayıları, bu yayınların toplam yayınlara oranları, bu çalışmada incelenen algoritmaları en çok kullanan araştırmacıların bulunduğu ülke sıralaması, ülkemizde bu çalışmalara katkısı olan üniversitelerin sıralaması ve algoritmaların yaygın olarak kullanıldığı bilim alanları hakkında bilgiler verilmiştir. Son aşamada ise yapılan yayınların eğilimleri Mann-Kendall test istatistiği ile araştırılmış ve gelecekteki yayın potansiyellerinden bahsedilmiştir. İncelenen algoritmalar arasında Kedi Sürüsü Algoritması dışındaki tüm algoritmalarda %95 güven aralığında artan bir trend tespit edilmiştir.

Keywords

Yapay Zeka Algoritmaları, Mann-Kendall, Yapay Sinir Ağları, Bulanık Mantık, ANFİS, Genetik Algoritmalar

References

• Adibifard, M., Tabatabaei-Nejad, S., ve Khodapanah, E. (2014). Artificial Neural Network (ANN) to estimate reservoir parameters in Naturally Fractured Reservoirs using well test data. Journal of Petroleum Science and Engineering, 122, 585-594.
• Aluclu, I., Dalgic, A., ve Toprak, Z. (2008). A fuzzy logic-based model for noise control at industrial workplaces. Applied Ergonomics, 39, 3, 368-378.
• Arias-Mendez, A., Warning, A., Datta, A., ve Balsa-Canto, E. (2013). Quality and safety driven optimal operation of deep-fat frying of potato chips. Journal of Food Engineering, 119, 1, 125-134.
• Arif, M., Kattan, A., ve Ahamed, S. (2015). Classification of physical activities using wearable sensors. Intelligent Automation ve Soft Computing, 23, 1, 21-30.
• Arslan, O. (2015). An Optımızatıon Model For The Management Of Multı-Objectıve Water Resources Systems Wıth Multıple Dams In Serıes. Fresenius Environmental Bulletin, 24, 10, 3100.
• Baccar, N., Jridi, M., ve Bouallegue, R. (2017). Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks. Wireless Personal Communications, 97, 2, 3165-3181.
• Bahrami, M., Bozorg, H., ve Chu, X. (2018). Application of Cat Swarm Optimization Algorithm for Optimal Reservoir Operation. Journal Of Irrıgatıon And Draınage Engıneerıng, 144, 1.
• Bilen, M., Işık, A., ve Yiğit, T. (2015). A Hybrid Artifical Neural Network-Genetic Algorithm Approach for Classification of Microarray Data., 339-342. Malatya/Türkiye.
• Chang, T., Talei, A., Alaghmand, S., ve Ooi, M. (2017). Choice of rainfall inputs for event-based rainfall-runoff modeling in a catchment with multiple rainfall stations using data-driven techniques. Journal of Hydrology, 545, 100-108.
• Chaudhur, S., Chowdhury, A., ve Das, P. (2018). Implementation of Sugeno: ANFIS for forecasting the seismic moment of large earthquakes over Indo-Himalayan region. Natural Hazards, 90, 1, 391-405.
• Chehreghan, A., ve Abbaspour, R. (2017). An Improvement on the Clustering of High-Resolution Satellite Images Using a Hybrid Algorithm. Journal of the Indian Society of Remate Sensing, 45, 4, 579-590.
• Chen, S., ve Zhang, T. (2018). Force control approaches research for robotic machining based on particle swarm optimization and adaptive iteration algorithms. Industrial Robot: the international journal of robotics research and application, 45, 1, 141-151.
• Ebtehaj, I., ve Bonakdari, H. (2017). Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport. Applied Water Science, 7, 8, 4287-4299.
• Erdoğan, H., ve Ozdemir, M. (2016). Neuro-Fuzzy Approach on Core Resistance Estimation at Loss Minimization Control of Permanent Magnet Synchronous Motor. Elektronıka Ir Elektrotechnıka, 22, 3, 7-12.
• Fuat, Z. (2009). Flow Discharge Modeling in Open Canals Using a New Fuzzy Modeling Technique (SMRGT). Clean-Soil Air Water, 37, 9, 742-752.Gholami, V., Booij, M., Tehrani, E., ve Hadian, M. (2018). Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data. Catena, 163, 210-218.
• Gill, J., ve Singh, J. (2017). Energetic and exergetic performance analysis of the vapor compression refrigeration system using adaptive neuro-fuzzy inference system approach. Experimental Thermal and Fluid Science, 88, 246-260.
• Gultekin, H., Coban, B., ve Akhlaglaghi, V. (2017). Cyclic scheduling of parts and robot moves in m-machine robotic cells. Computers ve Operations Research, 90, 161-172.
• Guo, X., Lu, Z., Cui, H., Lui, B., Jianng, Q., ve Wang, S. (2018). Modelling and solving the position tracking problem of remote-controlled gastrointestinal drug-delivery capsules. Biomedical Signal Processing and Control, 39, 213-218.
• Haklidir, F., ve Haklidir, M. (2017). Fuzzy control of calcium carbonate and silica scales in geothermal systems. Geothermics, 70, 230-238.
• Haydo, P. (2018). From Morphological Analysis to optimizing complex industrial operation scenarios. Technological Forecasting and Social Change, 126, 147-160.
• Haznedar, B., Arslan, M., ve Kalınlı, A. (2017). Karaciğer Mikroarray Kanser Verisinin Sınıflandırılması için Genetik Algoritma Kullanarak ANFIS’in eğitilmesi. Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21, 1, 54-62.
• Kayadelen, C., Taşkıran, T., ve Günaydın, O. (2009). Adaptive neuro-fuzzy modeling for the swelling potential of compacted soils. Environmental Earth Sciences, 59, 109-115.Kendall, M. (1975). Rank Correlation Methods. London: Charles Griffin.
• Keskin, M., ve Taylan, E. (2009). Artificial Models for Interbasin Flow Prediction in Southern Turkey. Journal of Hydrologic Engineering, 14, 7, 752-758.
• Kuscu, O., ve Kucuksille, E. (2011). Heuristic Methods in Vehicle Routing Systems. Elektronıka Ir Elektrotechnıka, 1, 65-70.
• Lerma, N., Paredes-Arquiola, L., Andreu, J., Solera, A., ve Sechi, G. (2015). Assessment of evolutionary algorithms for optimal operating rules design in real Water Resource Systems. Environmental Modelling ve Software 69, 425-436.
• Liu, D., Hu, Y., Fu, Q., ve Imran, K. (2016). Optimizing channel cross-section based on cat swarm optimization. Water Scıence And Technology-Water Supply, 19, 1, 219-228.
• Ma, R., Wu, H., ve Ding, L. (2017). Artificial bee colony optimised controller for small-scale unmanned helicopter. Aeronautical Journal, 121, 1246, 1879-1896.
• Majumde, P., ve Eldho, T. (2016). A New Groundwater Management Model by Coupling Analytic Element Method and Reverse Particle Tracking with Cat Swarm Optimization. Water Resources Management, 30, 6, 1953-1972.
• Mann, H. (1945). Non-parametric test against trend. Econometrika, 13, 245-259.
• Marwana, A., Zhoua, M., Abdelrehim, M., ve Meschke, G. (2016). Optimization of artificial ground freezing in tunneling in the presence of seepage flow. Computers and Geotechnics, 75, 112-125.
• Niu, W., Feng, Z., Cheng, C., ve Zhou, J. (2018). Forecasting Daily Runoff by Extreme Learning Machine Based on Quantum-Behaved Particle Swarm Optimization. Journal of Hydrologic Engineering, 23, 3.
• Nourani, V. (2017). An Emotional ANN (EANN) approach to modeling rainfall-runoff process. Journal of Hydrology , 544, 267-277.
• Özdemir, G., Aydemir, E., Olgun, M., ve Mulbay, Z. (2016). Forecasting of Turkey natural gas demand using a hybrid algorithm. Energy Sources Part B-Economıcs Plannıng And Polıcy, 11, 4, 295-302.
• Öztemel, E. (2012). Yapay Sinir Ağları. İstanbul: Papatya Yayıncılık Eğitim.
• Pappula, L., ve Ghosh, D. (2017). Synthesis of linear aperiodic array using Cauchy mutated cat swarm optimization. International Journal of Electronics and Communications (AEÜ), 72, 52–64.
• Pellet, N., Giordano, F., Dar, M., Gregori, G., Zakeeruddin, S., Maier, J., ve Gratzel, M. (2017). Hill climbing hysteresis of perovskite-based solar cells: a maximum power point tracking investigation. Progress in Photovoltaics, 25, 11, 942-950.
• Perez, C., Vega-Rodriguez, M., Reder, K., ve Floerke, M. (2017). A Multi-Objective Artificial Bee Colony-based optimization approach to design water quality monitoring networks in river basins. Journal of Cleaner Production, 166, 579-589.
• Quej, V., Almoroxa, J., Arnaldob, J., ve Saitoc, L. (2017). ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment. Journal of Atmospheric and Solar–Terrestrial Physics, 156, 62-70.
• Sahin, U., ve Bedirhanoglu, I. (2014). A Fuzzy Model Approach to Stress–Strain Relationship of Concrete in Compression. Arabian Journal for Science and Engineering, 39, 6, 4514-4527.
• Salim, O. (2017). New neuro-fuzzy system-based holey polymer fibers drawing process. Aıp Advances, 7, 10.
• Saplıoğlu, K., Kilit, M., ve Yavuz, B. K. (2014). Trend analysis of streams in the western mediterranean basin of Turkey. Fresenius Environmental Bulletin, 23, 1a, 313-324.
• Saplıoğlu, K., Küçükerdem, T., ve Alqaysi, R. (2017). Akdeniz Bölgesi akarsularının su kalitesi sınıflarının ve trendlerinin belirlenmesi. Dicle Üniversitesi Mühendislik dergisi, 8(1), 33-42.
• Saplıoğlu, M. (2010). An Accident Prediction Model for Urban Uncontrolled Intersections. Isparta, Turkey : Natural and Applied Sciences, Süleyman Demirel Uni. (in Turkish).
• Seferoglu, H., ve Modiano, E. (2016). Separation of Routing and Scheduling in Backpressure-Based Wireless Networks. IEEE/ACM Transactions on Networking (TON), 24, 3, 1787-1800.
• Shabani A, M., ve Mazahery, A. (2011). The ANN application in FEM modeling of mechanical properties of Al–Si alloy. Applied Mathematical Modelling, 35, 5707-5713.Shen, Q., Shi, W., ve Kong, W. (2008). Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data. Computational Biology and Chemistry, 32, 1, 53-60.
• Song, M., Chen, K., ve Wang, J. (2018). Three-dimensional wind turbine positioning using Gaussian particle swarm optimization with differential evolution. Journal of Wind Engineering and Industrial Aerodynamics, 172, 317-324.
• Sun, H., Yang, C., Lin, C., Pan, J., Snasel, V., ve Abraham, A. (2015). A New Cat Swarm Optimization with Adaptive Parameter Control. Genetıc And Evolutıonary Computıng, 329, 69-78.
• Temel, S., Unaldı, N., ve Kaynak, O. (2014). On Deployment of Wireless Sensors on 3-D Terrains to Maximize Sensing Coverage by Utilizing Cat Swarm Optimization with Wavelet Transform. Ieee Transactıons On Systems Man Cybernetıcs-Systems, 44, 1, 111-120.
• Termeh, S., Kornejady, A., Pourghasemi, H., ve Keesstra, S. (2018). Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic a, lgorithms. Science of The Total Environment, 615, 438-451.
• Toksoz, D., Yilmaz, I., Nefeslioglu, H., ve Marschalko, M. (2016). A fuzzy classification routine for fine-grained soils. Quarterly Journal Of Engıneerıng Geology And Hydrogeology, 49, 4, 344-349.
• Turkeli, E., ve Ozturk, H. (2017). Optimum design of partially prestressed concrete beams using Genetic Algorithms. Structural Engineering and Mechanics, 64, 5, 579-589.
• Ülengin, F., Işık, M., Ekici, Ş., Özaydın, Ö., Kabak, Ö., ve Topcu, E. (2017). Policy developments for the reduction of climate change impacts by the transportation sector. Transport Policy, 61, 36-50.
• Wu, S., Chan, T., Hsieh, M., ve Lin, C. (2016). Quantum evolutionary algorithm and tabu search in pressurized water reactor loading pattern design. Annals of Nuclear Energy, 94, 773-782.
• Xie, X., He, F., Xu, D., Dong, J., Cheng, S., ve Wu, Z. (2012). Application of large-scale integrated vertical-flow constructed wetland in Beijing Olympic forest park: design, operation and performance. Water and Environment Journal, 26, 1, 100-107.
• Yidirim, A., Gunes, F., ve Belen, M. (2017). Dıfferentıal Evolutıon Optımızatıon Applıed To The Performance Analysıs Of A Mıcrowave Transıstor. Sıgma Journal Of Engıneerıng And Natural Scıences, 8, 2, 135-144