alt="Graph depicts the accuracy vs. hidden layer."/>
Figure 1.11 Accuracy vs. hidden layer.
From the above-mentioned graphs, it is evident that the accuracy at hidden layer 4 is good. So, we will be predicting the difficult of code 8 with the ANN model with four hidden layers and Bernoulli’s RBM.
The ANN model predicts that the person feels the unknown code easy by a probability of 0.530607.
The Bernoulli RBM model predicts that the person feels the unknown code easy by a probability of 0.679071.
1.6 Discussion
The data collected was quite less when compared to the data required for ANN. The less data accounts for the less accuracy generated by the model. We fail to consider higher number of hidden layers since there is a chance of over fitting. The main reason to generate the graphs of accuracy vs. epochs is to see how the accuracy is changing with respect to epochs. The main reason to generate the graph of accuracy vs. hidden layer is to see which number of hidden layers is best to make the prediction. This model can be very much helpful in the software industries to make predictions on how much stress a person would face if he is to do a coding part of a project. So, the company can give the required instruction to the employee and suggest some methods which can increase the skill level of the person which reduce the mental work load while he does the project and also improving the capacity of the employee. We can also use this in the universities to see how much stress a student faces when he tries to solve programming questions. Using this data, the university officials can tweak their teaching policies and try to improvise the skill level of the student and help in improving the new generation day by day.
1.7 Advantages and Disadvantages of the Study
The advantages of the study are multifold. First, it helps academicians and industry professionals to understand a novel process of mental workload prediction and analysis. Second, it contributes in the application of deep learning in mental work load prediction. Third, based on the authors’ knowledge, this is first which provides an application of deep learning in prediction of mental workload during code debugging. The disadvantage of the study can be attributed to the use of small data set for the prediction of mental workload.
1.8 Limitations of the Study
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.
1.9 Conclusion
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.
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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
2.1 Introduction
Artificial neural network (ANN) is one of the most successfully implemented tools in the domain of machine learning. As the name suggests, ANN mimics human nervous system. The nervous