Atıflarım

Google Akademik H-Index : 6

Scopus H-Index : 5

  1. M. F. Adak, N. Duru, H. T. Duru, “Elevator simulator design and estimating energy consumption of an elevator system”, Energy Build., vol. 65, pp. 272–280, 2013. (SCI-EXP) [13 Atıf]
    1. Zhang, Y., Yan, Z., Yuan, F., Yao, J., & Ding, B. (2019). A Novel Reconstruction Approach to Elevator Energy Conservation Based on a DC Micro-Grid in High-Rise Buildings. Energies, 12(1), 33. (SCI-EXP Atıf)
    2. Murshed, S., Duval, A., Koch, A., & Rode, P. (2019). Impact of urban morphology on energy consumption of vertical mobility in Asian cities—a comparative analysis with 3D city models. Urban Science, 3(1), 4. (Uluslararası Dergi Atıf)
    3. Tukia, T., Uimonen, S., Siikonen, M. L., Donghi, C., & Lehtonen, M. (2018). High-resolution modeling of elevator power consumption. Journal of Building Engineering, 18, 210-219. (ESCI Atıf)
    4. Çiflikli, Cebrail, and Emre Öner Tartan. “Grup Asansör Sistemlerinin Simülasyonu.” Engineer & the Machinery Magazine 59.693 (2018). (Ulusal Dergi Atıf)
    5. Esteban, Ekaitz, et al. “Model-Based Estimation of Elevator Rail Friction Forces.” Advances in Condition Monitoring of Machinery in Non-Stationary Operations. Springer International Publishing, 2016. 363-374. (Uluslararası Kitaptan Atıf)
    6. Tukia, Toni, et al. “Explicit method to predict annual elevator energy consumption in recurring passenger traffic conditions.” Journal of Building Engineering, 8 (2016): 179-188. (ESCI Atıf)
    7. Zhao, B., Quan, L., Hao, Y., Research of operating characteristics and energy efficiency of traction elevator with hybrid electric-hydraulic drive, (2016) Jixie Gongcheng Xuebao/Journal of Mechanical Engineering, 52 (4), pp. 192-198. (Scopus Atıf)
    8. Kong, Dong-Seok, et al. “Development of an End-use Analysis Tool for Existing Buildings Based on Energy Billing Data.” Korean Journal of Air-Conditioning and Refrigeration Engineering 27.3 (2015): 128-136. (Uluslararası Dergi Atıf)
    9. Ayaz, M. Dişlisiz Asansör Sistemleri İçin Alüminyum Sargılı Sabit Mıknatıslı Senkron Motor Tasarımı ve Maliyet Analizi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7(1), 115-123. (Ulusal Dergi Atıf)
    10. Zhang, Y., Yan, Z., Yuan, F., Yao, J., & Ding, B. (2019). A Novel Reconstruction Approach to Elevator Energy Conservation Based on a DC Micro-Grid in High-Rise Buildings. Energies, 12(1), 33. [SCI-EXP]
    11. Tukia, T., Uimonen, S., Siikonen, M. L., Donghi, C., & Lehtonen, M. (2019). Modeling the aggregated power consumption of elevators–the New York city case study. Applied Energy, 251, 113356. [SCI-EXP]
    12. Murshed, S., Duval, A., Koch, A., & Rode, P. (2019). Impact of urban morphology on energy consumption of vertical mobility in Asian cities—a comparative analysis with 3D city models. Urban Science, 3(1), 4. [Alan-indeks]
    13. AYAZ, M . (2019). Dişlisiz Asansör Sistemleri İçin Alüminyum Sargılı Sabit Mıknatıslı Senkron Motor Tasarımı ve Maliyet Analizi. Düzce Üniversitesi Bilim ve Teknoloji Dergisi , 7 (1) , 115-123 . DOI: 10.29130/dubited.428300 [Ulakbim-TR]
  2. M. F. Adak, N. Yumusak, “Classification of E-Nose Aroma Data of Four Fruit Types by ABC-Based Neural Network”, Sensors, vol. 16, no. 3, p. 304, Feb. 2016. (SCI-EXP) [21 Atıf]
    1. Hu, W., Wan, L., Jian, Y., Ren, C., Jin, K., Su, X., … & Wu, W. (2019). Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing. Advanced Materials Technologies, 4(2), 1800488. (SCI-EXP Atıf)
    2. Güneşer, M. T. (2019). Artificial intelligence solution to extract the dielectric properties of materials at sub-THz frequencies. IET Science, Measurement & Technology. (Uluslararası Dergi Atıf)
    3. Bharathi, S. K. V., Sukitha, A., Moses, J. A., & Anandharamakrishnan, C. (2019). Instrument-based detection methods for adulteration in spice and spice products–A review. Journal of Spices and Aromatic Crops, 27(2), 106-118. (Uluslararası Dergi Atıf)
    4. Oroian, M., & Ropciuc, S. (2018). Botanical authentication of honeys based on Raman spectra. Journal of Food Measurement and Characterization, 12(1), 545-554. (SCI-EXP Atıf)
    5. Mochalski, P., Ruzsanyi, V., Wiesenhofer, H., & Mayhew, C. A. (2018). Instrumental sensing of trace volatiles—a new promising tool for detecting the presence of entrapped or hidden people. Journal of breath research, 12(2), 027107. (SCI-EXP Atıf)
    6. Yang, J., & Peng, Z. (2018). Improved abc algorithm optimizing the bridge sensor placement. Sensors, 18(7), 2240. (SCI-EXP Atıf)
    7. Zhang, Y. D., Dong, Z., Chen, X., Jia, W., Du, S., Muhammad, K., & Wang, S. H. (2017). Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimedia Tools and Applications, 1-20. (SCI-EXP Atıf)
    8. Liu, Fuqi, Hua Chen, and Xuxiang Tang. “Investigation on strawberry freshness by rapid determination using artificial olfactory system.” International Journal of Food Properties just-accepted (2017). (SCI-EXP Atıf)
    9. Wei, Z., Xiao, X., Wang, J., & Wang, H. (2017). Identification of the rice wines with different marked ages by electronic nose coupled with smartphone and cloud storage platform. Sensors, 17(11), 2500. (SCI-EXP Atıf)
    10. Abbatangelo, M., Núñez-Carmona, E., Sberveglieri, V., Zappa, D., Comini, E., & Sberveglieri, G. (2018). Application of a novel S3 nanowire gas sensor device in parallel with GC-MS for the identification of rind percentage of grated Parmigiano Reggiano. Sensors, 18(5), 1617. (SCI-EXP Atıf)
    11. Uçar, Ayşegül, and Recep Özalp. “Efficient android electronic nose design for recognition and perception of fruit odors using Kernel Extreme Learning Machines.” Chemometrics and Intelligent Laboratory Systems (2017). (SCI-EXP Atıf)
    12. Ozturk, Turgut, et al. “Extracting the dielectric constant of materials using ABC-based ANNs and NRW algorithms.” Journal of Electromagnetic Waves and Applications 30.13 (2016): 1785-1799. (SCI-EXP Atıf)
    13. Ozturk, T., Elhawil, A., Düğenci, M., Ünal, İ., & Uluer, İ. (2016). Extracting the dielectric constant of materials using ABC-based ANNs and NRW algorithms. Journal of ElEctromagnEtic WavEs and applications, 30(13), 1785-1799. (SCI-EXP Atıf)
    14. Jiang, Ye, et al. “Weighted Global Artificial Bee Colony Algorithm Makes Gas Sensor Deployment Efficient.” Sensors 16.6 (2016): 888. (SCI-EXP Atıf)
    15. Zhang, Y. D., Dong, Z., Chen, X., Jia, W., Du, S., Muhammad, K., & Wang, S. H. (2019). Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimedia Tools and Applications, 78(3), 3613-3632.   [SCI-EXP]
    16. Hu, W., Wan, L., Jian, Y., Ren, C., Jin, K., Su, X., … & Wu, W. (2019). Electronic Noses: From Advanced Materials to Sensors Aided with Data Processing. Advanced Materials Technologies, 4(2), 1800488.  [SCI-EXP]
    17. Jia, W., Liang, G., Jiang, Z., & Wang, J. (2019). Advances in electronic nose development for application to agricultural products. Food Analytical Methods, 12(10), 2226-2240. [SCI-EXP]
    18. Güneşer, M. T. (2019). Artificial intelligence solution to extract the dielectric properties of materials at sub-THz frequencies. IET Science, Measurement & Technology, 13(4), 523-528. [SCI-EXP]
    19. Gradišek, A., van Midden, M., Koterle, M., Prezelj, V., Strle, D., Štefane, B., … & Muševič, I. (2019). Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning. Sensors, 19(23), 5207. [SCI-EXP]
    20. Misbah, M., Rivai, M., & Kurniawan, F. (2019). Quartz crystal microbalance based electronic nose system implemented on Field Programmable Gate Array. TELKOMNIKA (Telecommunication Computing Electronics and Control), 17(1), 370. [Alan-indeks]
    21. Graboski, A. M., Ballen, S. C., Steffens, J., & Steffens, C. Hyphenated Electronic Nose Technique for Aroma Analysis of Foods and Beverages. Food Aroma Evolution, 177. [Uluslararası Kitap]
  3. M. F. Adak, M. Akpinar, N. Yumusak “Determination of the Gas Density in Binary Gas Mixtures Using Multivariate Data Analysis”, IEEE Sensors Journal, vol.17 pp, no. 11, p. 3288-3297, Jun. 2017. (SCI-EXP) [3 Atıf]
    1. Länge, K. (2019). Bulk and Surface Acoustic Wave Sensor Arrays for Multi-Analyte Detection: A Review. Sensors, 19(24), 5382. [SCI-EXP]
    2. Zhao, X., Wen, Z., Pan, X., Ye, W., & Bermak, A. (2019). Mixture Gases Classification Based on Multi-Label One-Dimensional Deep Convolutional Neural Network. IEEE Access, 7, 12630-12637. (SCI-EXP Atıf)
    3. Shiba, K., Tamura, R., Sugiyama, T., Kameyama, Y., Koda, K., Sakon, E., … & Yoshikawa, G. (2018). Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis. ACS sensors, 3(8), 1592-1600. (SCI-EXP Atıf)
  4. M. F. Adak, N. Yumusak, “Studies on Usability of Mobile Applications: Review”, Global J. on Tech., vol. 6, pp. 37–43, 2014. [2 Atıf]
    1. Hussain, Azham Bin, et al. “Usability Evaluation of Mobile Game Applications: A Systematic Review.” International Journal of Computer and Information Technology (2015): 5. (Uluslararası Dergi Atıf)
    2. Bicen, Huseyin, and Saide Sadikoglu. “Determination of the Opinions of Students on Tourism Impact Using Mobile Applications.” Procedia Economics and Finance 39 (2016): 270-274. (ScienceDirect Atıf)
  5. M. Akpinar, M. F. Adak, N. Yumusak, “Forecasting natural gas consumption with hybrid neural networks — Artificial bee colony” in 2016 2nd International Conference on Intelligent Energy and Power Systems (IEPS), 2016, pp. 1–6, Kiew/Ukraine. [4 Atıf]
    1. Akpinar, Mustafa, and Nejat Yumusak. “Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods.” Energies 9.9 (2016): 727. (SCI-EXP Atıf)
    2. Qiao, W., Huang, K., Azimi, M., & Han, S. (2019). A Novel Hybrid Prediction Model for Hourly Gas Consumption in Supply Side Based on Improved Machine Learning Algorithms. IEEE Access. [SCI-EXP]
    3. Liu, J., & Meng, L. (2019). Integrating Artificial Bee Colony Algorithm and BP Neural Network for Software Aging Prediction in IoT Environment. IEEE Access, 7, 32941-32948. [SCI-EXP]
    4. Li, H., Lu, Y., Zheng, C., Yang, M., & Li, S. (2019). Groundwater Level Prediction for the Arid Oasis of Northwest China Based on the Artificial Bee Colony Algorithm and a Back-propagation Neural Network with Double Hidden Layers. Water, 11(4), 860. [SCI-EXP]