An Enhanced Approach for Segmentation of Liver from Computed Tomography Images

287-293An accurate segmentation of liver from Computed Tomography (CT) scans is essential for liver tumor research as it offers valuable information for clinical diagnosis and treatment. However, it is challenging to achieve an accurate segmentation of the liver because of the blurred edges, low contrast and similar intensity of the organs in the CT scan. In this paper, an automated model which will segment the liver from CT images using a hybrid algorithm has been used. The segmentation of liver from CT scan is done with the help of Particle Swarm Optimization (PSO) followed by level set algorithm. The ultimate aim of using this hybrid algorithm is to improve the accuracy of liver segmentation. Computer aided classification of liver CT into healthy and tumorous images aids in diagnosis of liver diseases. It can help a great deal in diagnosis of liver disorders. In order to achieve better classification results, it is of high importance to segment the liver accurately without an error of over or under segmentation. The results obtained indicate that the approach used in this work is faster and has 98.62% accuracy, 99.2% specificity, 97.1% sensitivity, 97.8% F-measure, 96.6% Matthews Coefficient Constant (MCC), 99.08% precision, 97.8% dice coefficient and 95.7% jaccard coefficient in segmenting the liver

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Intrusion Detection System based on Chaotic Opposition for IoT Network

The rapid advancement of network technologies and protocols has fueled the widespread endorsement of the Internet of Things (IoT) in numerous domains, including everyday life, healthcare, industries, agriculture, and more. However, this rapid growth has also given rise to numerous security concerns within IoT systems. Consequently, privacy and security have become paramount issues in the IoT framework. Due to the heterogeneous data produced by smart IoT devices, traditional intrusion detection system doesn\u27t work well with IoT system. The massive volume of heterogeneous data has several irrelevant, redundant, and unnecessary features which lead to high computation time and low accuracy of IDS. Therefore, to tackle these challenges, this paper presents a novel metaheuristic-based IDS model for the IoT systems. The chaotic opposition-based Harris Hawk optimization (CO-IHHO) algorithm is used to perform the feature selection of data traffic. The chosen features are subsequently inputted into a machine learning (ML) classifier to detect network traffic intrusions. The performance of the CO-IHHO based IDS model is verified against the BoT-IoT dataset. Experimental findings reveal that CO-IHHO-DT achieves the maximal accuracy of 99.65% for multiclass classification and 100% for binary classification, and minimal computation time of 31.34 sec for multiclass classification and 133.54 sec for binary classification

An efficient robust hyperheuristic clustering algorithm

Observations on recent research of clustering problems illustrate that most of the approaches used to deal with these problems are based on meta-heuristic and hybrid meta-heuristic to improve the solutions. Hyperheuristic is a set of heuristics, meta- heuristics and high-level search strategies that work on the heuristic search space instead of solution search space. Hyperheuristics techniques have been employed to develop approaches that are more general than optimization search methods and traditional techniques. In the last few years, most studies have focused considerably on the hyperheuristic algorithms to find generalized solutions but highly required robust and efficient solutions. The main idea in this research is to develop techniques that are able to provide an appropriate level of efficiency and high performance to find a class of basic level heuristic over different type of combinatorial optimization problems. Clustering is an unsupervised method in the data mining and pattern recognition. Nevertheless, most of the clustering algorithms are unstable and very sensitive to their input parameters. This study, proposes an efficient and robust hyperheuristic clustering algorithm to find approximate solutions and attempts to generalize the algorithm for different cluster problem domains. Our proposed clustering algorithm has managed to minimize the dissimilarity of all points of a cluster using hyperheuristic method, from the gravity center of the cluster with respect to capacity constraints in each cluster. The algorithm of hyperheuristic has emerged from pool of heuristic techniques. Mapping between solution spaces is one of the powerful and prevalent techniques in optimization domains. Most of the existing algorithms work directly with solution spaces where in some cases is very difficult and is sometime impossible due to the dynamic behavior of data and algorithm. By mapping the heuristic space into solution spaces, it would be possible to make easy decision to solve clustering problems. The proposed hyperheuristic clustering algorithm performs four major components including selection, decision, admission and hybrid metaheuristic algorithm. The intensive experiments have proven that the proposed algorithm has successfully produced robust and efficient clustering results

Hybrid Theory-Machine Learning Methods for the Prediction of AFP Layup Quality

The advanced manufacturing capabilities provided through the automated fiber placement (AFP) system has allowed for faster layup time and more consistent production across a number of different geometries. This contributes to the modern production of large composite structures and the widespread adaptation of composites in industry in general and aerospace in particular. However, the automation introduced in this process increases the difficulty of quality assurance efforts. Industry available tools for predicting layup quality are either limited in scope, or have extremely high computational overhead. With the advent of automated inspection systems, direct capture of semantic inspection data, and therefore complete quality data, becomes available. It is therefore the aim of this document to explore and develop a technique to combine semantic inspection data and incomplete but fast physical modeling tool into a comprehensive hybridized model for predicting and optimizing AFP layup quality. To accomplish this, a novel parameterization of Gaussian Process Regression is developed such that nominal behavior is dictated through theory and analytic models, with latent variables being accounted for in the stochastic aspect of the model. Coupled with a unique clustering approach for data representation, it is the aim of this model to improve on the current state of the art in quality prediction as well as provide a direct path to process parameter optimization

Prediction of sulfur content in diesel fuel using fluorescence spectroscopy and a hybrid ant colony : Tabu Search algorithm with polynomial bases expansion

It is widely accepted that feature selection is an essential step in predictive modeling. There are several approaches to feature selection, from filter techniques to meta-heuristics wrapper methods. In this paper, we propose a compilation of tools to optimize the fitting of black-box linear models. The proposed AnTSbe algorithm combines Ant Colony Optimization and Tabu Search memory list for the selection of features and uses l1 and l2 regularization norms to fit the linear models. In addition, a polynomial combination of input features was introduced to further explore the information contained in the original data. As a case study, excitation-emission matrix fluorescence data were used as the primary measurements to predict total sulfur concentration in diesel fuel samples. The sample dataset was divided into S10 (less than 10 ppm of total sulfur), and S100 (mean sulfur content of 100 ppm) groups and local linear models were fit with AnTSbe. For the Diesel S100 local models, using only 5 out of the original 1467 fluorescence pairs, combined with bases expansion, we were able to satisfactorily predict total sulfur content in samples with MAPE of less than 4% and RMSE of 4.68 ppm, for the test subset. For the Diesel S10 local models, the use of 4 Ex/Em pairs was sufficient to predict sulfur content with MAPE 0.24%, and RMSE of 0.015 ppm, for the test subset. Our experimental results demonstrate that the proposed methodology was able to satisfactorily optimize the fitting of linear models to predict sulfur content in diesel fuel samples without need of chemical of physical pre-treatment, and was superior to classic PLS regression methods and also to our previous results with ant colony optimization studies in the same dataset. The proposed AnTSbe can be directly applied to data from other sources without need for adaptations

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm