Multi-stage optimization of a deep model: A case study on ground motion modeling.

In this study, a multi-stage optimization procedure is proposed to develop deep neural network models which results in a powerful deep learning pipeline called intelligent deep learning (iDeepLe). The proposed pipeline is then evaluated by a challenging real-world problem, the modeling of the spectral acceleration experienced by a particle during earthquakes. This approach has three main stages to optimize the deep model topology, the hyper-parameters, and its performance, respectively. This pipeline optimizes the deep model via adaptive learning rate optimization algorithms for both accuracy and complexity in multiple stages, while simultaneously solving the unknown parameters of the regression model. Among the seven adaptive learning rate optimization algorithms, Nadam optimization algorithm has shown the best performance results in the current study. The proposed approach is shown to be a suitable tool to generate solid models for this complex real-world system. The results also show that the parallel pipeline of iDeepLe has the capacity to handle big data problems as well

Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

This paper aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction for nicotine-dependent patients and future treatment efficacy. Two classes of patients were studied. One class took the drug N-acetylcysteine and the other class took a placebo. Then, the patients underwent a double-blind smoking cessation treatment and the resting-state fMRI scans of their brains before and after treatment were recorded. The scientific research goal of this study was to interpret the fMRI connectivity maps based on machine learning algorithms to predict the patient who will relapse and the one who will not. In this regard, the feature matrix was extracted from the image slices of brain employing voxel selection schemes and data reduction algorithms. Then, the feature matrix was fed into the machine learning classifiers including optimized CART decision tree and Naive-Bayes classifier with standard and optimized implementation employing 10-fold cross-validation. Out of all the data reduction techniques and the machine learning algorithms employed, the best accuracy was obtained using the singular value decomposition along with the optimized Naive-Bayes classifier. This gave an accuracy of 93% with sensitivity-specificity of 99% which suggests that the relapse in nicotine-dependent patients can be predicted based on the resting-state fMRI images. The use of these approaches may result in clinical applications in the future

Detection and isolation of black hole attack in mobile ad hoc networks: A review

© 2020 SPIE. Mobile Ad hoc Network or MANET is a wireless network that allows communication between the nodes that are in range of each other and are self-configuring. The distributed administration and dynamic nature of MANET makes it vulnerable to many kind of security attacks. One such attack is Black hole attack which is a well known security threat. A node drops all packets which it should forward, by claiming that it has the shortest path to the destination. Intrusion Detection system identifies the unauthorized users in the system. An IDS collects and analyses audit data to detect unauthorized users of computer systems. This paper aims in identifying Black-Hole attack against AODV with Intrusion Detection System, to analyze the attack and find its countermeasure

Building energy consumption forecast using multi-objective genetic programming

A multi-objective genetic programming (MOGP) technique with multiple genes is proposed to formulate the energy performance of residential buildings. Here, it is assumed that loads have linear relation in terms of genes. On this basis, an equation is developed by MOGP method to predict both heating and cooling loads. The proposed evolutionary approach optimizes the most significant predictor input variables in the model for both accuracy and complexity, while simultaneously solving the unknown parameters of the model. In the proposed energy performance model, relative compactness has the most and orientation the least contribution. The proposed MOGP model is simple and has a high degree of accuracy. The results show that MOGP is a suitable tool to generate solid models for complex nonlinear systems with capability of solving big data problems via parallel algorithms

Stock Risk Assessment via Multi-Objective Genetic Programming

Recent exponential growth of investors in stock markets brings the idea to develop a predictive model to forecast the total risk of investment in stock markets. In this paper, an evolutionary approach was proposed to predict the total risk in stock investment based on an S&P 500 database in a time period of 1991-2010 employing a multi-objective genetic programming along with an adaptive regression by mixing algorithm. The reasonable results suggest that the proposed model can be applied to various stock databases to assess the total risk of investment. The proposed model along with stock selection decision support systems can overcome the disadvantages of weighted scoring stock selection

An Interpretable Deep Learning Framework for Health Monitoring Systems: A Case Study of Eye State Detection using EEG Signals

Effective monitoring and early detection of deterioration in patients play an essential role in healthcare. This includes minimizing the number of emergency encounters, reducing the length of hospitalization stay, re-admission rates of the patients, and etc. Cutting-edge methods in artificial intelligence (AI) have the ability to significantly improve outcomes. However, the struggle to interpret these black box models presents a serious problem to the healthcare industry. When selecting a model, the decision to sacrifice accuracy for interpretability must be made. In this paper, we propose an interpretable framework with the ability of real-time prediction. To demonstrate the predictive power of the framework, a case study on eye state detection using electroencephalogram (EEG) signals was employed to investigate how a deep neural network (DNN) model makes a prediction, and how that prediction can be interpreted. The promising results can be used to employ more advanced models in healthcare solutions without any concern of sacrificing the interpretation

An Evolutionary Framework for Real-Time Fraudulent Credit Detection

Fraud has been a worldwide issue that is facing the major economies of the world. Within an economical system, undetected and unpunished fraudulent activities can erode the public trust in law enforcement institutions and even incentivize more fraud. Therefore, detection of fraudulent activities and prosecution of responsible entities is of utmost importance for financial regulatory bodies around the globe. Of the challenges rising with this task is the scarcity of detection resources (auditors) and the fraudsters constantly adapting to the new circumstances of the market. To address these issues, this paper proposes an evolutionary framework for credit fraud detection with the ability to incorporate (and adapt to) the incoming data in real-time. The goal of the framework is to identify the entities with high a risk of fraud for efficient targeting of the scarce resources. The data that is generated as a result of the audits are fed into the framework for further training