OctaSOM - An octagonal based SOM lattice structure for biomedical problems

In this study, an octagonal-based self-organizing network’s lattice structure is proposed to allow more exploration and exploitation in updating the weights for better mapping and classification performances.The neighborhood of the octagonal-based lattice structure provides more nodes for the weights updating than standard hexagonal-based lattice structure. Based on our experiment, the octagonal-based lattice structure performance is better than standard hexagonal lattice structure on biomedical datasets for classification problem. This indicates that proposed algorithm is an alternative lattice structure for self-organizing network which give more wisdom to classification problems especially in the biomedical domains

GPUMLib: Deep Learning SOM Library for Surface Reconstruction

The evolution of 3D scanning devices and innovation in computer processing power and storage capacity has sparked the revolution of producing big point-cloud datasets. This phenomenon has becoming an integral part of the sophisticated building design process especially in the era of 4th Industrial Revolution. The big point-cloud datasets have caused complexity in handling surface reconstruction and visualization since existing algorithms are not so readily available. In this context, the surface reconstruction intelligent algorithms need to be revolutionized to deal with big point-cloud datasets in tandem with the advancement of hardware processing power and storage capacity. In this study, we propose GPUMLib – deep learning library for self-organizing map (SOM-DLLib) to solve problems involving big point-cloud datasets from 3D scanning devices. The SOM-DLLib consists of multiple layers for reducing and optimizing those big point cloud datasets. The findings show the final objects are successfully reconstructed with optimized neighborhood representation and the performance becomes better as the size of point clouds increases

Preliminary study on learning challenges in machine learning-based flight delay prediction

Machine learning based flight delay prediction is one of the numerous real-life application domains where the problem of imbalance in class distribution is reported to affect the performance of learning algorithms. However, the fact that learning algorithms have been reported to perform well on some class imbalance problems posits the possibility of other contributing factors. In this study, we visually explore air traffic data after dimensionality reduction with t-Distributed Stochastic Neighbour Embedding. Our initial findings suggest a high degree of overlapping between the delayed and on-time class instances which can be a greater problem for learning algorithms than class imbalance

Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm

Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning

The need for polymorphic encryption algorithms: A review paper

Current symmetric ciphers including the Advanced Encryption Standard (AES) are deterministic and open. Using standard ciphers is necessary for interoperability. However, it gives the potential opponent significant leverage, as it facilitates all the knowledge and time he needs to design effective attacks. In this review paper, we highlight prominent contributions in the field of symmetric encryption. Furthermore, we shed light on some contributions that aim at mitigating potential threats when using standard symmetric ciphers. Furthermore, we highlight the need for more practical contributions in the direction of polymorphic or multishape ciphers

MapReduce a comprehensive review

MapReduce encompasses a framework in the processing and management of large scale datasets within a distributed cluster. The framework has been employed in several applications including search indexes generation, analysis of access log, document clustering, and other data analytics. A flexible computation model is adopted in MapReduce in addition to plain interface which comprises the functions of map and reduce. The interface is customizable based on application developers. MapReduce has captured the interest among many scholars whereby the interest has been on increasing its usability and efficiency in support to database-centric operations. Accordingly, this paper provides a complete review regarding a vast continuum of proposals and systems concentrating basically on the support of distributed data management and processing with the use of the framework of MapReduce

Artificial fish swarm optimization for multilayer network learning in classification problems

Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN.In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems.The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. The results are compared to other NIC methods, i.e., Particle Swarm Optimization (PSO) and Differential Evolution (DE), in which AFSA gives better accuracy with feasible performance for all datasets

Tissue-like P system for Segmentation of 2D Hexagonal Images

Membrane computing, which is a new computational model inspired by the structure and functioning of biological cells and by the way the cells are organized in tissues. MC has been adopted in many real world applications including image segmentation. In contrast to the traditional square grid for representing and sampling digital images, hexagonal grid is an alternative efficient mechanism which can better represents and visualizes the curved objects. In this paper, a tissue-like P system with region-based and edge-based segmentation is used to segment two dimensional hexagonal images, wherein P-Lingua programming language is used to implement and validate the proposed system. The achieved experimental results clearly demonstrated the effectiveness of using hexagonal connectivity to segment two dimensional images in a less number of rules and computational steps. Moreover, the results reveal that this approach has the potential of segmenting large images in few number of steps

An improved chaotic image encryption algorithm

Chaotic-based image encryption algorithms are countless in number. Encryption techniques based on Chaos are among the most effectual algorithms for encryption of data image. In past works, chaos-based cryptosystems applied the chaotic dynamical system with the linkage to the harmonization of two chaotic systems and controls. Good performances have resulted but there were several downsides pertaining to the single rule usage by each, impacting security, privacy and dependability of the techniques mentioned. Serious problems were also documented in their usage in satellite imagery. As a possible solution, a novel chaos-based symmetric method of key cryptosystem is proposed in this paper. This method employs external secret key that Logistic, Henon and Gauss iterated maps have previously expanded. For creating the secret key matrix for image encryption, these maps are merged. Here, simple logical XOR and multiple key generation processes were applied. Assessment to the method is performed on the satellite images dataset, and security is evaluated through the experimental analysis. As evidenced, the chaos-based satellite image cryptosystem demonstrates appropriateness for satellite image encryption and decryption in the preservation of security and dependability of the storage and transmission process

Statistical and nature-inspired metaheuristics analysis on flexirubin production

Nowadays, demand for natural pigments has increased dramatically due to the awareness of the toxicity of some synthetic pigments. Because of the high cost of growth medium for natural pigment production, various studies have been carried out to explore medium which are less costly, such as agricultural waste. This study highlight on the application of firefly algorithm (FA) and bat algorithm (BA) in optimizing yellowish-orange pigment production (flexirubin) from the agricultural waste material. At present, response surface methodology (RSM) is the most preferred statistical method in optimizing pigment production. However, in the last two decades, nature-inspired metaheuristics approach has been used extensively in the fermentation process and have continually improve the efficiency in the optimization problem especially in pigment production. This study compared the analytics studies of RSM, FA and BA in the estimation of fermentation parameters (Lactose, Ltryptophan, and KH2PO4) in flexirubin production from Chryseobacterium artocarpi CECT8497T. All models provided similar quality predictions for the above three independent variables in term of flexirubin production with bat algorithm showing more accurate in estimation, with the coefficient value of 98.87% compare to RSM 98.20% and FA 98.38%