Handbook of Intelligent Computing and Optimization for Sustainable Development. Группа авторов
Чтение книги онлайн.

Читать онлайн книгу Handbook of Intelligent Computing and Optimization for Sustainable Development - Группа авторов страница 30

СКАЧАТЬ code debugging. The disadvantage of the study can be attributed to the use of small data set for the prediction of mental workload.

      The analysis related to this study has been conducted based on the small dataset of eye movements collected during the code debugging. As the dataset is small, the results which have been obtained out of the analysis may be misleading in terms of prediction and accuracy.

      In this paper, a deep learning has been proposed for the prediction of mental workload based on eye movements’ data. First, the data set is collected based on the eye movements during code debugging. Second, the raw eye movement data has been pre-processed and features are extracted for the further analysis. Third, the analysis has been conducted based on the deep learning models. As we can see that the accuracy of the model is quite low, so in the future works, we would try to make a new dataset with more data which can be easily fed to any deep learning network and also work on tuning some parameters of the artificial neural networks which help in increasing the accuracy of the model. The work load prediction which we obtained from the deep learning tool shows that the 8th code which was of unknown difficulty is easy to the person debugging the code. But the probability is slightly >0.5 so doing with more other codes will help in tuning our model and help to increase the accuracy of the predictions.

      1. Sweller, J., Van Merrienboer, J.J.G., Paas, F.G., Cognitive architecture and instructional design. Educ. Psychol. Rev., 10, 3, 251–296, 1998.

      2. Others and Borghini, G., Aricò, P., Di Flumeri, G., Cartocci, G., Colosimo, A., Bonelli, S., Golfetti, A., Imbert, J.P., Granger, G., Benhacene, R., EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers. Sci. Rep., 7, 1, 1–16, 2017.

      3. Wang, S., Gwizdka, J., Chaovalitwongse, W.A., Using wireless EEG signals to assess memory workload in the $ n $-back task. IEEE Trans. Hum-Mach Syst., 46, 3, 424–435, 2015.

      4. Zhong, Y. and Zhang, J., Identification of temporal variations in mental workload using locallylinear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques. Comput. Methods Programs Biomed., 115, 3, 119–134, 2014.

      5. Li, X., Chen, Z., Liang, Q., Yang, Y., Analysis of mental stress recognition and rating based on hidden Markov model. J. Comput. Inf. Syst., 10, 18, 7911–7919, 2014.

      6. Penaranda, B.N. and Baldwin, C.L., Temporal factors of eeg and artificial neural network classifiers of mental workload, in: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, pp. 188–192, 2012.

      7. Palinko, O., Kun, A.L., Shyrokov, A., Heeman, P., Estimating cognitive load using remote eye tracking in a driving simulator, in: Proceedings of the 2010 symposium on eye-tracking research \& applications, pp. 141–144, 2010.

      8. Krejtz, K., Duchowski, A.T., Niedzielska, A., Biele, C., Krejtz, I., Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze. PLoS One, 13, 9, 1–23, 2018.

      1 *Corresponding author: [email protected]

      2

      Artificial Neural Networks in DNA Computing and Implementation of DNA Logic Gates

       Mandrita Mondal1* and Kumar S. Ray2

       1Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

       2Computer Engineering and Applications, GLA University, Mathura, India

       Abstract

      The activity of the brain resembles the computer as it functions as an input-output device. Artificial neural network (ANN) can be defined as the computing system developed for simulation of human nervous system. It consists of huge number of vastly interconnected processing units, termed as neurons. ANN is one of the most successfully implemented tools in the domain of machine learning. In this chapter, we discuss the development of ANN using short DNA strands, i.e., oligonucleotides. The short sequences of DNA molecules can be used to code input and output signal and to build the basic architecture of the neuron. We also illustrate the design methodology of DNA logic gates and DNA logic circuits which are the basic of Boolean algebra. Because of few drawbacks, viz., immense energy consumption, vast memory requirement, and heat dissipation, the traditional computation is approaching toward the limitations of its processing power and design strategy. The unique property of DNA molecules to store, process, and retrieve information motivates the notion of this unconventional computation, DNA computing. Our aim is the paradigm shift in computational world; from silicon to carbon. The design strategies discussed in this chapter are essential for effective development of DNA computer practically.

      Keywords: Artificial neural networks, DNA logic gates, DNA logic circuits, perceptrons, DNA computing, deoxyribozyme, strand displacement

      Neural networks or percetrons have been cultivated since 1940s and it has become a competent domain of artificial intelligence. It consists of input layer, hidden layer, and output layer. The hidden layer is consists of one or more layers depending on the complexity of the problem. The units of hidden layer transform the input into something that the output layer can use. Neural networks are used as efficient tools to generate complex patterns which are quiet impossible for a human programmer to extract and teach the machine to recognize. ANN has several computational applications, such as speech recognition, pattern classification, machine diagnostics, target recognition, process modeling and control, and medical diagnosis.

      The intention of this chapter is to theoretically build the backbone of DNA computer. DNA molecules are competent to store, process, and retrieve information which motivates the notion of DNA computing. In this chapter, we focus to replace the mathematical and logical aspect of artificial intelligence by the unique chemical properties and characteristics of DNA molecules. DNA computing is the emerging domain in computational world and the modern technology is gradually approaching toward the paradigm shift, from silicon to carbon, where DNA computing is overcoming the drawbacks of classical silicon-based computing. We demonstrate the use of DNA oligonucleotides to develop the models of ANN. The input and output signal, which constructs the basic architecture of the neuron, has been encoded in terms of short DNA sequences.

      The activity of the brain resembles the computer as it functions as an input-output device. The basic design of digital computer follows Boolean СКАЧАТЬ