Classification of Alzheimer Disease with Molecular Communication Systems using LSTM

Yıl 2022, Cilt: 8 Sayı: 2, 25 - 31, 31.07.2022

İbrahim Işık

https://doi.org/10.22399/ijcesen.1061006

Öz

Today, there are many diseases caused by cell or inter molecular communication. For example, a communication disorder in the nerve nano-network can cause very serious nervous system-related diseases such as Multiple Sclerosis (MS), Alzheimer's and Paralysis. Understanding these diseases caused by communication is very important in order to develop innovative treatment methods inspired by information technologies. In addition, many advanced environmental and industrial nano-sensor networks such as the development of biologically inspired Molecular Communication systems (MCs), cellular-accurate health monitoring systems, many medical applications such as the development of communication-capable nano-implants for nervous system diseases. Nano networks focused on communication between nano-sized devices (Nano Machines) is a new communication concept which is known as MCs in literature. In this study, on the contrary to the literature, a new Long Short-Term Memory (LSTM) based MC model has been used to analyse the proposed system. After obtained the number of received molecules for different number of Amyloid Beta (Aβ) which causes Alzheimer’, a new method based on the LSTM model of deep learning is used for the classification of Aβ. Finally it is obtained that when the number of Aβ increases, the number of received molecules decrease. On a data set with five classes, experiments are conducted using LSTM. The proposed model's accuracy, precision, and sensitivity values are obtained as 97.05, 98.59 and 98.54 percent, respectively. The categorization procedure of the findings generated from the designed model appears to be performing well.

Anahtar Kelimeler

nano communication, deep learning LSTM, LSTM

Kaynakça

• [1] T. H. Yutaka Okaie, Shouhei Kobayashi Tadashi Nakano, Yasushi Hiraoka, Tokuko Haraguchi, “Methods and Applications of Mobile Molecular Communication,” Proc. IEEE, vol. 107, no. 7, 2019, doi: doi: 10.1109/JPROC.2019.2917625.
• [2] Nariman Farsad, “Molecular Communication,” York university, 2014.
• [3] K. A. and S. R. M. Djazia Leila Benmansour, Abdelhakim Kaci, Abdelmoumen Anis Bousahla, Houari Heireche, Abdelouahed Tounsi, Afaf S. Alwabli, Alawiah M. Alhebshi, “The nano scale bending and dynamic properties of isolated protein microtubules based on modified strain gradient theory,” Adv. Nano Res., vol. 7, no. 6, 2019.
• [4] A. B. and B. K. Ismail Bensaid, “Dynamic analysis of higher order shear-deformable nanobeams resting on elastic foundation based on nonlocal strain gradient theory,” Adv. Nano Res., vol. 6, no. 3, 2018.
• [5] A. A. and Y. Z. Wael A. Altabey, Mohammad Noori, “Nano-delamination monitoring of BFRP nano-pipes of electrical potential change with ANNs,” Adv. Nano Res., vol. 9, no. 1, 2020.
• [6] E. Isik and H. Toktamis, “TLD characteristic of glass, feldspathic and lithium disilicate ceramics,” Luminescence, vol. 34, no. 2, pp. 272–279, 2019, doi: 10.1002/bio.3605.
• [7] H. B. Yilmaz, A. C. Heren, and T. Tugcu, “3-D Channel Characteristics for Molecular Communications with an Absorbing Receiver,” IEEE Commun. Lett. 3-D, pp. 1–4, 2014.
• [8] S. L. and Y.-S. Shon, “Molecular interactions between pre-formed metal nanoparticles and graphene families,” Adv. Nano Res., vol. 6, no. 4, 2018.
• [9] E. Isik, “Analyzing of the Viscosity by Using Artificial Neural Networks,” J. Phys. Chem. Funct. Mater., vol. 3, no. 2, pp. 72–76, 2020.
• [10] L. Lin, Q. Wu, M. Ma, and H. Yan, “Concentration-based demodulation scheme for mobile receiver in molecular communication,” Nano Commun. Netw., vol. 20, pp. 11–19, 2019, doi: 10.1016/j.nancom.2019.01.003.
• [11] S. Huang, L. Lin, W. Guo, H. Yan, J. Xu, and F. Liu, “Initial Distance Estimation and Signal Detection for Diffusive Mobile Molecular Communication,” IEEE Trans. Nanobioscience, vol. 19, no. 3, pp. 422–433, 2020, doi: 10.1109/TNB.2020.2986314.
• [12] G. Wu and P. Tseng, “A Deep Neural Network-Based Indoor Positioning Method using Channel State Information,” pp. 290–294, 2021.
• [13] C. M. A. Niitsoo, T. Edelhäußer, E. Eberlein, N. Hadaschik, “A Deep Learning Approach to Position Estimation from Channel Impulse Responses,” Sensors, vol. 1, no. D, pp. 1–23, 2018, doi: 10.3390/s19051064.
• [14] N. Farsad and A. Goldsmith, “Neural Network Detectors for Sequence Detection in Communication Systems,” pp. 1–15.
• [15] D. J. Selkoe et al., “The role of APP processing and trafficking pathways in the formation of amyloid β-protein,” Ann. N. Y. Acad. Sci., vol. 777, no. 617, pp. 57–64, 1996, doi: 10.1111/j.1749-6632.1996.tb34401.x.
• [16] Y. Zhou et al., “Amyloid beta: structure, biology and structure-based therapeutic development,” Acta Pharmacol. Sin., vol. 38, no. 9, pp. 1205–1235, 2017, doi: 10.1038/aps.2017.28.
• [17] M. T. Barros, W. Silva, and C. D. M. Regis, “The Multi-Scale Impact of the Alzheimer’s Disease in the Topology Diversity of Astrocytes Molecular Communications Nanonetworks,” no. October, pp. 1–16, 2018, [Online]. Available: http://arxiv.org/abs/1810.09294.
• [18] H. A. Pearson and C. Peers, “Physiological roles for amyloid β peptides,” vol. 1, pp. 5–10, 2006, doi: 10.1113/jphysiol.2006.111203.
• [19] Q. Wu, L. Lin, Z. Luo, and H. Yan, “Bit alignment scheme for mobile receiver in molecular communication,” Int. Conf. Ubiquitous Futur. Networks, ICUFN, pp. 750–752, 2017, doi: 10.1109/ICUFN.2017.7993892.
• [20] F. Walsh, “Protocols for Molecular Communication,” Waterford Institute of Technology, 2013.
• [21] E. Isik, “Analyzing of the diffusion constant on the nano-scale systems by using artificial neural networks,” AIP Adv., vol. 11, no. 10, Oct. 2021, doi: 10.1063/5.0067795.
• [22] I. Isik, “How Mobility of Transmitter and Receiver Effect the Communication Quality,” vol. XX, pp. 1–5, 2017.
• [23] A. Tomar and N. Gupta, “Prediction for the spread of COVID-19 in India and effectiveness of preventive measures,” Sci. Total Environ., vol. 728, p. 138762, Aug. 2020, doi: 10.1016/J.SCITOTENV.2020.138762.
• [24] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.
• [25] A. A. Chowdhury, K. T. Hasan, and K. K. S. Hoque, “Analysis and Prediction of COVID-19 Pandemic in Bangladesh by Using ANFIS and LSTM Network,” Cognit. Comput., vol. 13, no. 3, pp. 761–770, 2021, doi: 10.1007/s12559-021-09859-0.
• [26] V. K. and S. K., “Towards activation function search for long short-term model network: A differential evolution based approach,” J. King Saud Univ. - Comput. Inf. Sci., 2020, doi: https://doi.org/10.1016/j.jksuci.2020.04.015.
• [27] X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010, pp. 249–256.
• [28] M. B. Er, E. Isik, and I. Isik, “Parkinson’s detection based on combined CNN and LSTM using enhanced speech signals with Variational mode decomposition,” Biomed. Signal Process. Control, vol. 70, p. 103006, Sep. 2021, doi: 10.1016/J.BSPC.2021.103006.
• [29] I. Isik, H. B. Yilmaz, and M. E. Tagluk, “A Preliminary Investigation of Receiver Models in Molecular Communication via Diffusion,” 2017.
• [30] E. Isik, I. Isik, and H. Toktamis, “Analysis and estimation of fading time from thermoluminescence glow curve by using artificial neural network,” Radiat. Eff. Defects Solids, 2021, doi: 10.1080/10420150.2021.1954000.