Simultaneous optimization of neural network weights and active nodes using metaheuristics

Optimization of neural network (NN) significantly influenced by the transfer function used in its active nodes. It has been observed that the homogeneity in the activation nodes does not provide the best solution. Therefore, the customizable transfer functions whose underlying parameters are subjected to optimization were used to provide heterogeneity to NN. For the experimental purpose, a meta-heuristic framework using a combined genotype representation of connection weights and transfer function parameter was used. The performance of adaptive Logistic, Tangent-hyperbolic, Gaussian and Beta functions were analyzed. In present research work, concise comparisons between different transfer function and between the NN optimization algorithms are presented. The comprehensive analysis of the results obtained over the benchmark dataset suggests that the Artificial Bee Colony with adaptive transfer function provides the best results in terms of classification accuracy over the particle swarm optimization and differential evolution

Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach

Selection of a representative set of features is still a crucial and challeng- ing problem in machine learning. The complexity of the problem increases when any of the following situations occur: a very large number of at- tributes (large dimensionality); a very small number of instances or time points (small-instance set). The rst situation poses problems for machine learning algorithm as the search space for selecting a combination of relevant features becomes impossible to explore in a reasonable time and with rea- sonable computational resources. The second aspect poses the problem of having insu cient data to learn from (insu cient examples). In this work, we approach both these issues at the same time. The methods we proposed are heuristics inspired from nature (in particular, from biology). We pro- pose a hybrid of two methods which has the advantage of providing a good learning from fewer examples and a fair selection of features from a really large set, all these while ensuring a high standard classi cation accuracy of the data. The methods used are antlion optimization (ALO), grey wolf opti- mization (GWO), and a combination of the two (ALO-GWO). We test their performance on datasets having almost 50,000 features and less than 200 instances. The results look promising while compared with other methods such as genetic algorithms (GA) and particle swarm optimization (PSO)

A blockchain protocol for authenticating space communications between satellites constellations

Blockchain has found many applications, apart from Bitcoin, in different fields and it has the potential to be very useful in the satellite communications and space industries. Decentralized and secure protocols for processing and manipulating space transactions of satellite swarms in the form of Space Digital Tokens (SDT) can be built using blockchain technology. Tokenizing space transactions using SDTs will open the door to different new blockchain-based solutions for the advancement of constellation-based satellite communications in the space industry. Developing blockchain solutions using smart contracts could be used in securely authenticating various P2P satellite communications and transactions within/between satellite swarms. To manage and secure these transactions, using the proposed SDT concept, this paper suggested a blockchain-based protocol called Proof of Space Transactions (PoST). This protocol was adopted to manage and authenticate the transactions of satellite constellations in a P2P connection. The PoST protocol was prototyped using the Ethereum blockchain and experimented with to evaluate its performance using four metrics: read latency, read throughput, transaction latency, and transaction throughput. The simulation results clarified the efficiency of the proposed PoST protocol in processing and verifying satellite transactions in a short time according to read and transaction latency results. Moreover, the security results showed that the proposed PoST protocol is secure and efficient in verifying satellite transactions according to true positive rate (TPR), true negative rate (TNR), and accuracy metrics. These findings may shape a real attempt to develop a new generation of Blockchain-based satellite constellation systems

Using Singular Value Decomposition (SVD) as a solution for search result clustering

There are many search engines in the web, but they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Singular Value Decomposition (SVD) as a very good solution for search results clustering. Results are presented by visualizing neural network. Neural network is responsive for reducing result dimension to two dimensional space and we are able to present result as a picture that we are able to analyze

Oblique and rotation double random forest

Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models’ core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we propose rotation based double random forest. In rotation based double random forests, transformation or rotation of the feature space is generated at each node. At each node different random feature subspace is chosen for evaluation, hence the transformation at each node is different. Different transformations result in better diversity among the base learners and hence, better generalization performance. With the double random forest as base learner, the data at each node is transformed via two different transformations namely, principal component analysis and linear discriminant analysis. In the second approach, we propose oblique double random forest. Decision trees in random forest and double random forest are univariate, and this results in the generation of axis parallel split which fails to capture the geometric structure of the data. Also, the standard random forest may not grow sufficiently large decision trees resulting in suboptimal performance. To capture the geometric properties and to grow the decision trees of sufficient depth, we propose oblique double random forest. The oblique double random forest models are multivariate decision trees. At each non-leaf node, multisurface proximal support vector machine generates the optimal plane for better generalization performance. Also, different regularization techniques (Tikhonov regularization, axis-parallel split regularization, Null space regularization) are employed for tackling the small sample size problems in the decision trees of oblique double random forest. The proposed ensembles of decision trees produce trees with bigger size compared to the standard ensembles of decision trees as bagging is used at each non-leaf node which results in improved performance. The evaluation of the baseline models and the proposed oblique and rotation double random forest models is performed on benchmark 121 UCI datasets and real-world fisheries datasets. Both statistical analysis and the experimental results demonstrate the efficacy of the proposed oblique and rotation double random forest models compared to the baseline models on the benchmark datasets.This work is supported by Science and Engineering Research Board (SERB), Government of India under Ramanujan Fellowship Scheme, Grant No. SB/S2/RJN-001/2016 , and Department of Science and Technology under Interdisciplinary Cyber Physical Systems (ICPS) Scheme grant no. DST/ICPS/CPS-Individual/2018/276 . We gratefully acknowledge the Indian Institute of Technology Indore for providing facilities and support