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Year 2021, Volume: 9 Issue: 1, 182 - 193, 29.01.2021

Pelin Yıldırım Taşer , Vahid Akram

https://doi.org/10.21541/apjes.765795

Abstract

Nowadays, the application areas of indoor positioning and person tracking systems are increasing day by day. Especially, in many fields such as patient, personnel, device and customer tracking systems and smart buildings and crowd forecasting, it is crucial to determine the location of humans or their presence in the environment. Generally, in the indoor tracking systems, a small device, that periodically sends a radio signal, is attached to the mobile target asset and the location of the asset is determined with the signals obtained from this device. However, in some environments, it is necessary to determine the location of assets that do not carry any signal transmitters on them. Hence, in the systems without mobile devices, the mobility in the environment is estimated by analyzing fluctuations in the radio signals. In these systems, there are some static devices at various points in the environment that send radio signals periodically and can receive signals sent by other devices. If an object moves in the environment, the mobility and density of mobile objects in the environment can be detected by analyzing the fluctuations in signal strength. However, in some cases, temporary but relatively violent fluctuations, which are not related to any movement, may occur in radio signals. These fluctuations reduce the sensitivity and accuracy of the system by causing false detections. Thanks to the success of machine learning techniques in revealing hidden patterns and complex relations among data, human detection systems based on machine learning techniques offer higher accuracy estimation ability than traditional methods. Therefore, in this study, machine learning algorithms are utilized for human detection in indoor environment. Within the experimental studies, 10 different traditional (Naive Bayes, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-nearest Neighbors (K-NN)) and decision tree-based (C4.5, Random Forest, Random Tree, REPTree, Decision Stump, and HoeffdingTree) classification algorithms were applied separately on a data set consisting of 23585 records, obtained from 3 different wireless sensor nodes in the indoor environment, and were compared according to accuracy rate and model build time performances. When the obtained results are examined, it is observed that all algorithms applied in the study offer over 80% success performance in the detection of human in indoor environments and the most successful algorithm is Random Forest with an accuracy rate of 99.68%. In addition, when the traditional and decision tree-based classification algorithms compared according to the average accuracy rates they provided, it is seen that decision tree-based algorithms present higher estimation ability with 95.78%.

Keywords

Indoor Human Detection Machine Learning Received Signal Strength Indicator Classification Wireless Sensor Networks

References

  • [1]. F. Zafari, A. Gkelias, and K. K. Leung, “A survey of indoor localization systems and Technologies,” IEEE Commun., vol. 21, no 3, pp. 2568-2599, 2019. [2]. O. Dağdeviren and V.K. Akram, “TinyOS Tabanlı Telsiz Duyarga Ağları için Bir Konumlandırma ve k-Bağlılık Denetleme Sistemi,” Bilişim Teknolojileri Dergisi, vol. 10, no 2, pp. 139-152, 2017. [3]. P. Kriz, F. Maly, and T. Kozel, “Improving Indoor Localization Using Bluetooth Low Energy Beacons,” Mob. Inf. Syst., vol. 2016, pp. 1–11, 2016.
  • [4]. N. Karimpour, B. Karaduman, A. Ural, M. Challenger, and O. Dagdeviren, “IoT based Hand Hygiene Compliance Monitoring,” in 2019 International Symposium on Networks, Computers and Communications (ISNCC), Istanbul, Turkey, 2019, pp. 1-6.
  • [5]. S. Chakraborty, S. K. Ghosh, A. Jamthe, and D. P. Agrawal, “Detecting Mobility for Monitoring Patients with Parkinson’s Disease at Home using RSSI in a Wireless Sensor Network,” Procedia Comput. Sci., vol. 19, pp. 956–961, 2013.
  • [6]. O. Kaltiokallio and M. Bocca, “Real-Time Intrusion Detection and Tracking in Indoor Environment through Distributed RSSI Processing,” in 2011 IEEE 17th International Conference on Embedded and Real-Time Computing Systems and Applications, Toyama, 2011, pp. 61-70.
  • [7]. T. Teixeira and A. Savvides, “Lightweight People Counting and Localizing for Easily Deployable Indoors WSNs,” IEEE J Sel. Top. Signal Process., vol. 2, no. 4, pp. 493–502, 2008.
  • [8]. M. Nakatsuka, H. Iwatani, and J. Katto, “A study on passive crowd density estimation using wireless sensors,” in 4th International Conference on Mobile Computing and Ubiquitous Networking, Miraikan, Tokyo Japan, 2008, pp. 1-6.
  • [9]. S. Shukri, L. M. Kamarudin, G.C. Cheik, R. Gunasagaran, A. Zakaria, K. Kamarudin, and S.N. Azemi, “Analysis of RSSI-based DFL for human detection in indoor environment using IRIS mote,” in 3rd International Conference on Electronic Design (ICED), Phuket, Thailand, 2016, pp. 216-221.
  • [10].A. Booranawong, N. Jindapetch, and H. Saito, “A System for Detection and Tracking of Human Movements Using RSSI Signals, “ IEEE Sens. J., vol. 18, no. 6, pp. 2531–2544, 2018.
  • [11].A. Booranawong, N. Jindapetch, and H. Saito, “Adaptive Filtering Methods for RSSI Signals in a Device-Free Human Detection and Tracking System,” IEEE Syst. J., vol. 13, no. 3, pp. 2998–3009, 2019.
  • [12].B. Mrazovac, M. Bjelica, D. Kukolj, B. Todorovic, and D. Samardzija, “A human detection method for residential smart energy systems based on Zigbee RSSI changes,” IEEE Trans. Consum. Electron., vol. 58, no. 3, pp. 819–824, 2012.
  • [13].Y. Jin, Z. Tian, M. Zhou, , Z. Li, and Z. Zhang, “A whole-home level intrusion detection system using WiFi-enabled IoT,” in 14th International Wireless Communications & Mobile Computing Conference (IWCMC), Limassol, Cyprus, 2018, pp. 494-499.
  • [14].O. Kaltiokallio, M.Bocca, and L. M. Eriksson, “Distributed RSSI processing for intrusion detection in indoor environments,” in Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, Stockholm, Sweden, 2010, pp. 404-405.
  • [15].T. Wang, D. Yang, S. Zhang, Y. Wu, and S. Xu, “Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi,” Sensors, vol. 19, no. 10, pp. 2335, 2019.
  • [16]. J.W. Davis, V. Sharma, A. Tyagi, and M. Keck, Human Detection and Tracking, Boston,MA: Springer, 2009.
  • [17].H. Chen, Q. Zhang, Y. Liu, Y. Yang, and Z. Guo, “A Tentative Study on Zigbee-Based Indoor Human Intrusion Detection,” Advances in Intelligent Systems and Computing. Recent Developments in Intelligent Computing, Communication and Devices, pp. 501–506, 2018.
  • [18].B. Chatfield and R. J. Haddad, “RSSI-based spoofing detection in smart grid IEEE 802.11 home area networks,” 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington D.C., USA, 2017, pp. 1-5.
  • [19].Y. Bao, L. Dong, Y. Zheng, and Y. Liu, “WiSafe: a real-time system for intrusion detection based on wifi signals,” in Proceedings of the ACM Turing Celebration Conference-China, Chengdu, China, 2019, pp. 1-5.
  • [20].D. Li, L. Deng, M. Lee, and H. Wang, “IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning,” Int. J. Inf. Manag., vol. 49, pp. 533–545, 2019. [21].T.Yoshida and Y.Taniguchi, “Estimating the number of people using existing wifi access point in indoor environment,” in Proceedings of the 6th European Conference of Computer Science (ECCS’15), Rome, Italy, 2015, pp. 46-53.
  • [22].Y. Yuan, C. Qiu, W. Xi, and J. Zhao, “Crowd density estimation using wireless sensor networks,” in Seventh international conference on mobile Ad-hoc and sensor networks, Beijing, China, 2011, pp. 138-145.
  • [23].N. Matsumoto, J. Kawasaki, M. Suzuki, S. Saruwatari, and T. Watanabe, “Crowdedness Estimation Using RSSI on Already-deployed Wireless Sensor Networks,” in 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia, 2019, pp. 1-7.
  • [24].D. Ghosh, P. Roy, C. Chowdhury, and S. Bandyopadhyay. “An ensemble of condition based classifiers for indoor localization,” in IEEE International conference on advanced networks and telecommunications systems (ANTS), Bangalore, India, 2016, pp. 1-6.
  • [25].D. Alshamaa, A. Chkeir, and F. Mourad-Chehade, “Target localization using machine learning and belief functions: Application for elderly people in indoor environments,” in 2019 IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 2019, pp. 1-6.
  • [26].H. Huang and S. Lin, “WiDet: Wi-Fi based device-free passive person detection with deep convolutional neural networks,” Comput. Commun., vol. 150, pp. 357-366, (2020).
  • [27].M. A. Al-qaness, “Device-free human micro-activity recognition method using WiFi signals”, Geo. Spat. Inf. Sci., vol. 22, no. 2, pp.128-137, 2019.
  • [28].E. Alpaydin, Introduction to machine learning. Cambridge, MA: The MIT Press, 2020.
  • [29].P. Yildirim and D. Birant, “Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions,” in 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, Alberobello, Italy, 2014, pp. 110-115.
  • [30].P. Yıldırım, D. Birant and T. Alpyıldız, “Improving prediction performance using ensemble neural networks in textile sector,” in 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, Turkey, 2017, pp. 639-644.
  • [31].Z. Zhang, “Introduction to machine learning: k-nearest neighbors,” Ann. Transl. Med., vol. 4, no. 11, 2016.
  • [32].S. Huang, N. Cai, P. P. Pacheco, S. Narandes, Y. Wang, and W. Xu, “Applications of Support Vector Machine (SVM) Learning in Cancer Genomics,” Cancer Genom. Proteom., vol. 15, no. 1, pp. 41-51, 2017.
  • [33].Y. Song and Y. Lu, “Decision tree methods: applications for classification and prediction,” Shanghai Arch. Psychiatry, vol. 27, no. 2, pp. 130-135, 2015.
  • [34].J. R. Quinlan, C4.5 - programs for machine learning. San Mateo, CA: Kaufmann, 1992.
  • [35]. X. Gao, J. Wen, and C. Zhang, “An Improved Random Forest Algorithm for Predicting Employee Turnover,” Math. Probl. Eng., vol. 2019, pp. 1–12, 2019.
  • [36].A. Onan, “Şirket İflaslarının Tahminlenmesinde Karar Ağacı Algoritmalarının Karşılaştırmalı Başarım Analizi,” Bilişim Teknolojileri Dergisi, vol. 8, no. 1, 2015.
  • [37]. K. H. Raviya and B. Gajjar, “Performance Evaluation of Different Data Mining Classification Algorithm Using WEKA,” Paripex Indian J., vol. 2, no. 1, pp. 19–21, 2012.
  • [38].S. Chen, B. Shen, X. Wang, and S.-J. Yoo, “A Strong Machine Learning Classifier and Decision Stumps Based Hybrid AdaBoost Classification Algorithm for Cognitive Radios,” Sensors, vol. 19, no. 23, p. 5077, 2019.
  • [39].A. Kumar, P. Kaur, and P. Sharma, “A Survey on Decision Tree Algorithms of Classification in Data Mining,” Int. J. Sci. Res., vol. 5, no. 4, pp. 2094–2097, 2016.
  • [40].Weka 3 - Data Mining with Open Source Machine Learning Software in Java, URL: https://www.cs.waikato.ac.nz/ml/weka/ (Erişim zamanı; Temmuz, 4, 2020).
  • [41].Wireless Measurement System - Memsic Inc., URL: http://www.memsic.com/userfiles/files/Datasheets/WSN/IRIS_Datasheet.pdf (Erişim zamanı; Temmuz, 4, 2020).
  • [42].P. Levis, S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, and Culler, D. “TinyOS: An operating system for sensor networks,” In: Weber W., Rabaey J.M., Aarts E. (eds) Ambient Intelligence. Springer, Berlin, Heidelberg.
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  • [44]. B. Lantz, Machine learning with R, Birmingham: Packt Publishing, 2015.

Kapalı Ortamlarda Gerçek Zamanlı Kişi Tespitinde Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Başarım Analizi

Year 2021, Volume: 9 Issue: 1, 182 - 193, 29.01.2021

Pelin Yıldırım Taşer , Vahid Akram

https://doi.org/10.21541/apjes.765795

Abstract

Günümüzde, iç mekan konumlandırma ve kişi takip sistemlerinin uygulama alanları her geçen gün artış göstermektedir. Özellikle, hasta, personel, cihaz ve müşteri takip sistemleri ile akıllı binalar ve kalabalık tahminleme gibi birçok alanda, kişilerin konumlarının veya mekan içerisinde bulunma durumlarının doğru tespiti büyük önem taşımaktadır. İç mekan konumlandırma sistemlerinde genellikle, hedef mobil varlığın üzerine periyodik olarak radyo sinyali gönderen küçük bir cihaz yerleştirilir ve bu cihazdan elde edilen sinyaller ile varlığın konumu belirlenir. Fakat bazı ortamlarda, üzerinde herhangi bir sinyal göndericisi taşımayan varlıkların konumlarının tespit edilmesine ihtiyaç duyulmaktadır. Dolayısıyla, mobil cihaz kullanılmayan radyo tabanlı takip sistemlerinde, radyo sinyallerinde meydana gelen dalgalanmalar analiz edilerek, ortamdaki hareketlilik tahmin edilmeye çalışılır. Bu sistemlerde, ortamın çeşitli noktalarına periyodik olarak radyo sinyalleri gönderen ve diğer cihazların gönderdiği sinyalleri alabilen cihazlar yerleştirilir. Ortamda bulunan herhangi bir nesnenin hareket etmesi durumunda, sinyal gücündeki dalgalanmalar analiz edilerek, ortamdaki hareketlilik ve yoğunluk tahmin edilebilir. Ancak bazı durumlarda, radyo sinyallerinde hareketten kaynaklanmayan, geçici ama nispeten şiddetli dalgalanmalar yaşanabilmektedir. Bu tür dalgalanmalar, yanlış tespitlere sebep olarak, sistemin hassasiyetini ve doğruluğunu düşürmektedir. Makine öğrenmesi tekniklerinin, veriler arasındaki gizli örüntü ve karmaşık ilişkileri ortaya çıkarmadaki başarıları sayesinde, makine öğrenmesi tekniklerine dayalı kişi tespit sistemleri, geleneksel yöntemlere göre doğruluğu daha yüksek tahminleme becerisi sunmaktadır. Dolayısıyla, bu çalışmada, kapalı ortamda kişi tespiti için makine öğrenmesi algoritmalarından yararlanılmıştır. Deneysel çalışmalar kapsamında, 10 farklı geleneksel (Naive Bayes, Çok Katmanlı Algılayıcı (MLP), Destek Vektör Makineleri (SVM) ve K-en Yakın Komşuluk (K-NN)) ve karar ağacı tabanlı (C4.5, Random Forest, Random Tree, REPTree, Decision Stump ve HoeffdingTree) sınıflandırma algoritmaları, kapalı ortamdaki 3 farklı telsiz duyarga düğümünden elde edilen ve 23585 kayıttan oluşan veri seti üzerinde ayrı ayrı uygulanmış, doğruluk oranı ve model oluşturma süresi performanslarına göre karşılaştırılmıştır. Elde edilen sonuçlar incelendiğinde, çalışmada uygulanan tüm algoritmaların kapalı alandaki kişi tespitinde %80’in üzerinde başarı performansı sunduğu ve en başarılı algoritmanın %99.68 doğruluk oranı ile Random Forest olduğu gözlemlenmiştir. Ayrıca, geleneksel ve karar ağacı tabanlı sınıflandırma algoritmaları sağlamış oldukları ortalama doğruluk oranlarına göre kıyaslandığında ise karar ağacı tabanlı algoritmaların %95.78 ile daha yüksek tahminleme becerisi sunduğu görülmektedir.

Keywords

İç Mekan Kişi Tespiti Makine Öğrenmesi RSSI Sınıflandırma Telsiz Duyarga Ağları

References

  • [1]. F. Zafari, A. Gkelias, and K. K. Leung, “A survey of indoor localization systems and Technologies,” IEEE Commun., vol. 21, no 3, pp. 2568-2599, 2019. [2]. O. Dağdeviren and V.K. Akram, “TinyOS Tabanlı Telsiz Duyarga Ağları için Bir Konumlandırma ve k-Bağlılık Denetleme Sistemi,” Bilişim Teknolojileri Dergisi, vol. 10, no 2, pp. 139-152, 2017. [3]. P. Kriz, F. Maly, and T. Kozel, “Improving Indoor Localization Using Bluetooth Low Energy Beacons,” Mob. Inf. Syst., vol. 2016, pp. 1–11, 2016.
  • [4]. N. Karimpour, B. Karaduman, A. Ural, M. Challenger, and O. Dagdeviren, “IoT based Hand Hygiene Compliance Monitoring,” in 2019 International Symposium on Networks, Computers and Communications (ISNCC), Istanbul, Turkey, 2019, pp. 1-6.
  • [5]. S. Chakraborty, S. K. Ghosh, A. Jamthe, and D. P. Agrawal, “Detecting Mobility for Monitoring Patients with Parkinson’s Disease at Home using RSSI in a Wireless Sensor Network,” Procedia Comput. Sci., vol. 19, pp. 956–961, 2013.
  • [6]. O. Kaltiokallio and M. Bocca, “Real-Time Intrusion Detection and Tracking in Indoor Environment through Distributed RSSI Processing,” in 2011 IEEE 17th International Conference on Embedded and Real-Time Computing Systems and Applications, Toyama, 2011, pp. 61-70.
  • [7]. T. Teixeira and A. Savvides, “Lightweight People Counting and Localizing for Easily Deployable Indoors WSNs,” IEEE J Sel. Top. Signal Process., vol. 2, no. 4, pp. 493–502, 2008.
  • [8]. M. Nakatsuka, H. Iwatani, and J. Katto, “A study on passive crowd density estimation using wireless sensors,” in 4th International Conference on Mobile Computing and Ubiquitous Networking, Miraikan, Tokyo Japan, 2008, pp. 1-6.
  • [9]. S. Shukri, L. M. Kamarudin, G.C. Cheik, R. Gunasagaran, A. Zakaria, K. Kamarudin, and S.N. Azemi, “Analysis of RSSI-based DFL for human detection in indoor environment using IRIS mote,” in 3rd International Conference on Electronic Design (ICED), Phuket, Thailand, 2016, pp. 216-221.
  • [10].A. Booranawong, N. Jindapetch, and H. Saito, “A System for Detection and Tracking of Human Movements Using RSSI Signals, “ IEEE Sens. J., vol. 18, no. 6, pp. 2531–2544, 2018.
  • [11].A. Booranawong, N. Jindapetch, and H. Saito, “Adaptive Filtering Methods for RSSI Signals in a Device-Free Human Detection and Tracking System,” IEEE Syst. J., vol. 13, no. 3, pp. 2998–3009, 2019.
  • [12].B. Mrazovac, M. Bjelica, D. Kukolj, B. Todorovic, and D. Samardzija, “A human detection method for residential smart energy systems based on Zigbee RSSI changes,” IEEE Trans. Consum. Electron., vol. 58, no. 3, pp. 819–824, 2012.
  • [13].Y. Jin, Z. Tian, M. Zhou, , Z. Li, and Z. Zhang, “A whole-home level intrusion detection system using WiFi-enabled IoT,” in 14th International Wireless Communications & Mobile Computing Conference (IWCMC), Limassol, Cyprus, 2018, pp. 494-499.
  • [14].O. Kaltiokallio, M.Bocca, and L. M. Eriksson, “Distributed RSSI processing for intrusion detection in indoor environments,” in Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, Stockholm, Sweden, 2010, pp. 404-405.
  • [15].T. Wang, D. Yang, S. Zhang, Y. Wu, and S. Xu, “Wi-Alarm: Low-Cost Passive Intrusion Detection Using WiFi,” Sensors, vol. 19, no. 10, pp. 2335, 2019.
  • [16]. J.W. Davis, V. Sharma, A. Tyagi, and M. Keck, Human Detection and Tracking, Boston,MA: Springer, 2009.
  • [17].H. Chen, Q. Zhang, Y. Liu, Y. Yang, and Z. Guo, “A Tentative Study on Zigbee-Based Indoor Human Intrusion Detection,” Advances in Intelligent Systems and Computing. Recent Developments in Intelligent Computing, Communication and Devices, pp. 501–506, 2018.
  • [18].B. Chatfield and R. J. Haddad, “RSSI-based spoofing detection in smart grid IEEE 802.11 home area networks,” 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington D.C., USA, 2017, pp. 1-5.
  • [19].Y. Bao, L. Dong, Y. Zheng, and Y. Liu, “WiSafe: a real-time system for intrusion detection based on wifi signals,” in Proceedings of the ACM Turing Celebration Conference-China, Chengdu, China, 2019, pp. 1-5.
  • [20].D. Li, L. Deng, M. Lee, and H. Wang, “IoT data feature extraction and intrusion detection system for smart cities based on deep migration learning,” Int. J. Inf. Manag., vol. 49, pp. 533–545, 2019. [21].T.Yoshida and Y.Taniguchi, “Estimating the number of people using existing wifi access point in indoor environment,” in Proceedings of the 6th European Conference of Computer Science (ECCS’15), Rome, Italy, 2015, pp. 46-53.
  • [22].Y. Yuan, C. Qiu, W. Xi, and J. Zhao, “Crowd density estimation using wireless sensor networks,” in Seventh international conference on mobile Ad-hoc and sensor networks, Beijing, China, 2011, pp. 138-145.
  • [23].N. Matsumoto, J. Kawasaki, M. Suzuki, S. Saruwatari, and T. Watanabe, “Crowdedness Estimation Using RSSI on Already-deployed Wireless Sensor Networks,” in 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia, 2019, pp. 1-7.
  • [24].D. Ghosh, P. Roy, C. Chowdhury, and S. Bandyopadhyay. “An ensemble of condition based classifiers for indoor localization,” in IEEE International conference on advanced networks and telecommunications systems (ANTS), Bangalore, India, 2016, pp. 1-6.
  • [25].D. Alshamaa, A. Chkeir, and F. Mourad-Chehade, “Target localization using machine learning and belief functions: Application for elderly people in indoor environments,” in 2019 IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 2019, pp. 1-6.
  • [26].H. Huang and S. Lin, “WiDet: Wi-Fi based device-free passive person detection with deep convolutional neural networks,” Comput. Commun., vol. 150, pp. 357-366, (2020).
  • [27].M. A. Al-qaness, “Device-free human micro-activity recognition method using WiFi signals”, Geo. Spat. Inf. Sci., vol. 22, no. 2, pp.128-137, 2019.
  • [28].E. Alpaydin, Introduction to machine learning. Cambridge, MA: The MIT Press, 2020.
  • [29].P. Yildirim and D. Birant, “Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions,” in 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, Alberobello, Italy, 2014, pp. 110-115.
  • [30].P. Yıldırım, D. Birant and T. Alpyıldız, “Improving prediction performance using ensemble neural networks in textile sector,” in 2017 International Conference on Computer Science and Engineering (UBMK), Antalya, Turkey, 2017, pp. 639-644.
  • [31].Z. Zhang, “Introduction to machine learning: k-nearest neighbors,” Ann. Transl. Med., vol. 4, no. 11, 2016.
  • [32].S. Huang, N. Cai, P. P. Pacheco, S. Narandes, Y. Wang, and W. Xu, “Applications of Support Vector Machine (SVM) Learning in Cancer Genomics,” Cancer Genom. Proteom., vol. 15, no. 1, pp. 41-51, 2017.
  • [33].Y. Song and Y. Lu, “Decision tree methods: applications for classification and prediction,” Shanghai Arch. Psychiatry, vol. 27, no. 2, pp. 130-135, 2015.
  • [34].J. R. Quinlan, C4.5 - programs for machine learning. San Mateo, CA: Kaufmann, 1992.
  • [35]. X. Gao, J. Wen, and C. Zhang, “An Improved Random Forest Algorithm for Predicting Employee Turnover,” Math. Probl. Eng., vol. 2019, pp. 1–12, 2019.
  • [36].A. Onan, “Şirket İflaslarının Tahminlenmesinde Karar Ağacı Algoritmalarının Karşılaştırmalı Başarım Analizi,” Bilişim Teknolojileri Dergisi, vol. 8, no. 1, 2015.
  • [37]. K. H. Raviya and B. Gajjar, “Performance Evaluation of Different Data Mining Classification Algorithm Using WEKA,” Paripex Indian J., vol. 2, no. 1, pp. 19–21, 2012.
  • [38].S. Chen, B. Shen, X. Wang, and S.-J. Yoo, “A Strong Machine Learning Classifier and Decision Stumps Based Hybrid AdaBoost Classification Algorithm for Cognitive Radios,” Sensors, vol. 19, no. 23, p. 5077, 2019.
  • [39].A. Kumar, P. Kaur, and P. Sharma, “A Survey on Decision Tree Algorithms of Classification in Data Mining,” Int. J. Sci. Res., vol. 5, no. 4, pp. 2094–2097, 2016.
  • [40].Weka 3 - Data Mining with Open Source Machine Learning Software in Java, URL: https://www.cs.waikato.ac.nz/ml/weka/ (Erişim zamanı; Temmuz, 4, 2020).
  • [41].Wireless Measurement System - Memsic Inc., URL: http://www.memsic.com/userfiles/files/Datasheets/WSN/IRIS_Datasheet.pdf (Erişim zamanı; Temmuz, 4, 2020).
  • [42].P. Levis, S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, and Culler, D. “TinyOS: An operating system for sensor networks,” In: Weber W., Rabaey J.M., Aarts E. (eds) Ambient Intelligence. Springer, Berlin, Heidelberg.
  • [43]. P. Nadkarni, (2016) Core Technologies: Data Mining and “Big Data”. In: Clinical Research Computing. Academic Press, Iowa City, IA, United States.
  • [44]. B. Lantz, Machine learning with R, Birmingham: Packt Publishing, 2015.
There are 41 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Pelin Yıldırım Taşer 0000-0002-5767-2700

Vahid Akram 0000-0002-4082-6419

Publication Date January 29, 2021
Submission Date July 7, 2020
Published in Issue Year 2021 Volume: 9 Issue: 1

Cite

IEEE P. Yıldırım Taşer and V. Akram, “Kapalı Ortamlarda Gerçek Zamanlı Kişi Tespitinde Makine Öğrenmesi Algoritmalarının Karşılaştırmalı Başarım Analizi”, APJES, vol. 9, no. 1, pp. 182–193, 2021, doi: 10.21541/apjes.765795.
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