Renyi’s entropy based multilevel thresholding using a novel meta-heuristics algorithm

Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively

A Survey on the Network Models applied in the Industrial Network Optimization

Network architecture design is very important for the optimization of industrial networks. The type of network architecture can be divided into small-scale network and large-scale network according to its scale. Graph theory is an efficient mathematical tool for network topology modeling. For small-scale networks, its structure often has regular topology. For large-scale networks, the existing research mainly focuses on the random characteristics of network nodes and edges. Recently, popular models include random networks, small-world networks and scale-free networks. Starting from the scale of network, this survey summarizes and analyzes the network modeling methods based on graph theory and the practical application in industrial scenarios. Furthermore, this survey proposes a novel network performance metric - system entropy. From the perspective of mathematical properties, the analysis of its non-negativity, monotonicity and concave-convexity is given. The advantage of system entropy is that it can cover the existing regular network, random network, small-world network and scale-free network, and has strong generality. The simulation results show that this metric can realize the comparison of various industrial networks under different models.Comment: 26 pages, 11 figures, Journa

Intelligent and Improved Self-Adaptive Anomaly based Intrusion Detection System for Networks

With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Intrusions or attacks on computers and networks are activities or attempts to jeopardize main system security objectives, which called as confidentiality, integrity and availability. They lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. There is a great need for an effective Network Intrusion Detection System (NIDS), which are security tools designed to interpret the intrusion attempts in incoming network traffic, thereby achieving a solid line of protection against inside and outside intruders. In this work, we propose to optimize a very popular soft computing tool prevalently used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel machine learning framework called “ISAGASAA”, based on Improved Self-Adaptive Genetic Algorithm (ISAGA) and Simulated Annealing Algorithm (SAA). ISAGA is our variant of standard Genetic Algorithm (GA), which is developed based on GA improved through an Adaptive Mutation Algorithm (AMA) and optimization strategies. The optimization strategies carried out are Parallel Processing (PP) and Fitness Value Hashing (FVH) that reduce execution time, convergence time and save processing power. While, SAA was incorporated to ISAGA in order to optimize its heuristic search. Experimental results based on Kyoto University benchmark dataset version 2015 demonstrate that our optimized NIDS based BPNN called “ANID BPNN-ISAGASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. Moreover, improvement of GA through FVH and PP saves processing power and execution time. Thus, our model is very much convenient for network anomaly detection.

Adaptive firefly algorithm for hierarchical text clustering

Text clustering is essentially used by search engines to increase the recall and precision in information retrieval. As search engine operates on Internet content that is constantly being updated, there is a need for a clustering algorithm that offers automatic grouping of items without prior knowledge on the collection. Existing clustering methods have problems in determining optimal number of clusters and producing compact clusters. In this research, an adaptive hierarchical text clustering algorithm is proposed based on Firefly Algorithm. The proposed Adaptive Firefly Algorithm (AFA) consists of three components: document clustering, cluster refining, and cluster merging. The first component introduces Weight-based Firefly Algorithm (WFA) that automatically identifies initial centers and their clusters for any given text collection. In order to refine the obtained clusters, a second algorithm, termed as Weight-based Firefly Algorithm with Relocate (WFAR), is proposed. Such an approach allows the relocation of a pre-assigned document into a newly created cluster. The third component, Weight-based Firefly Algorithm with Relocate and Merging (WFARM), aims to reduce the number of produced clusters by merging nonpure clusters into the pure ones. Experiments were conducted to compare the proposed algorithms against seven existing methods. The percentage of success in obtaining optimal number of clusters by AFA is 100% with purity and f-measure of 83% higher than the benchmarked methods. As for entropy measure, the AFA produced the lowest value (0.78) when compared to existing methods. The result indicates that Adaptive Firefly Algorithm can produce compact clusters. This research contributes to the text mining domain as hierarchical text clustering facilitates the indexing of documents and information retrieval processes

Area laws and efficient descriptions of quantum many-body states

It is commonly believed that area laws for entanglement entropies imply that a quantum many-body state can be faithfully represented by efficient tensor network states - a conjecture frequently stated in the context of numerical simulations and analytical considerations. In this work, we show that this is in general not the case, except in one dimension. We prove that the set of quantum many-body states that satisfy an area law for all Renyi entropies contains a subspace of exponential dimension. Establishing a novel link between quantum many-body theory and the theory of communication complexity, we then show that there are states satisfying area laws for all Renyi entropies but cannot be approximated by states with a classical description of small Kolmogorov complexity, including polynomial projected entangled pair states (PEPS) or states of multi-scale entanglement renormalisation (MERA). Not even a quantum computer with post-selection can efficiently prepare all quantum states fulfilling an area law, and we show that not all area law states can be eigenstates of local Hamiltonians. We also prove translationally invariant and isotropic instances of these results, and show a variation with decaying correlations using quantum error-correcting codes.Comment: 5+2 pages, 3 figures; v2 added section on correlation

Energy-Efficient Technologies for High-Performance Manufacturing Industries

Ph.DDOCTOR OF PHILOSOPH

Area laws for the entanglement entropy - a review

Physical interactions in quantum many-body systems are typically local: Individual constituents interact mainly with their few nearest neighbors. This locality of interactions is inherited by a decay of correlation functions, but also reflected by scaling laws of a quite profound quantity: The entanglement entropy of ground states. This entropy of the reduced state of a subregion often merely grows like the boundary area of the subregion, and not like its volume, in sharp contrast with an expected extensive behavior. Such "area laws" for the entanglement entropy and related quantities have received considerable attention in recent years. They emerge in several seemingly unrelated fields, in the context of black hole physics, quantum information science, and quantum many-body physics where they have important implications on the numerical simulation of lattice models. In this Colloquium we review the current status of area laws in these fields. Center stage is taken by rigorous results on lattice models in one and higher spatial dimensions. The differences and similarities between bosonic and fermionic models are stressed, area laws are related to the velocity of information propagation, and disordered systems, non-equilibrium situations, classical correlation concepts, and topological entanglement entropies are discussed. A significant proportion of the article is devoted to the quantitative connection between the entanglement content of states and the possibility of their efficient numerical simulation. We discuss matrix-product states, higher-dimensional analogues, and states from entanglement renormalization and conclude by highlighting the implications of area laws on quantifying the effective degrees of freedom that need to be considered in simulations.Comment: 28 pages, 2 figures, final versio