A Hybrid Lightweight System for Early Attack Detection in the IoMT Fog.

Cyber-attack detection via on-gadget embedded models and cloud systems are widely used for the Internet of Medical Things (IoMT). The former has a limited computation ability, whereas the latter has a long detection time. Fog-based attack detection is alternatively used to overcome these problems.

Deepint.net: A Rapid Deployment Platform for Smart Territories.

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases.

Identifying a Gene Knockout Strategy Using a Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis to Enhance the Production of Succinate and Lactate in Escherichia Coli.

In recent years, metabolic engineering has gained central attention in numerous fields of science because of its capability to manipulate metabolic pathways in enhancing the expression of target phenotypes. Due to this, many computational approaches that perform genetic manipulation have been developed in the computational biology field. In metabolic engineering, conventional methods have been utilized to upgrade the generation of lactate and succinate in E.

A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models.

Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silico gene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies.

Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme.

Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data.

A Review of Gene Knockout Strategies for Microbial Cells.

Background: Predicting the effects of genetic modification is difficult due to the complexity of metabolic net- works. Various gene knockout strategies have been utilised to deactivate specific genes in order to determine the effects of these genes on the function of microbes. Deactivation of genes can lead to deletion of certain proteins and functions.

Gene knockout identification using an extension of Bees Hill Flux Balance Analysis.

Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout.

Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.

Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes.

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes.

Background: Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes.

A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data.

Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset.

VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics.

In this letter, neural networks (NNs) classify alcoholics and nonalcoholics using features extracted from visual evoked potential (VEP). A genetic algorithm (GA) is used to select the minimum number of channels that maximize classification performance. GA population fitness is evaluated using fuzzy ARTMAP (FA) NN, instead of the widely used multilayer perceptron (MLP).

Identification of the defective transmission devices using the wavelet transform.

In this paper, a system is described that uses the wavelet transform to automatically identify the particular failure mode of a known defective transmission device. The problem of identifying a particular failure mode within a costly failed assembly is of benefit in practical applications. In this system, external acoustic sensors, instead of intrusive vibrometers, are used to record the acoustic data of the operating transmission device.

Rotation-invariant neural pattern recognition system estimating a rotation angle.

A rotation-invariant neural pattern recognition system, which can recognize a rotated pattern and estimate its rotation angle, is considered. It is well-known that humans sometimes recognize a rotated form by means of mental rotation. The occurrence of mental rotation can be explained in terms of the theory of information types.

Temperature regulation with neural networks and alternative control schemes.

In this article, we compare the neuro-control algorithm to three other control algorithms: fuzzy logic control, generalized predictive control, and proportional-plus-integral control. Each of these four algorithms is implemented on a water bath temperature control system. The four systems are compared through experimental studies under identical conditions with respect to set-point regulation, the effect of unknown load disturbances, large parameter variation, and variable deadtime in the system.

High speed paper currency recognition by neural networks.

In this paper a new technique is proposed to improve the recognition ability and the transaction speed to classify the Japanese and US paper currency. Two types of data sets, time series data and Fourier power spectra, are used in this study. In both cases, they are directly used as inputs to the neural network.

Rotation-invariant neural pattern recognition system with application to coin recognition.

In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network.

A new back-propagation algorithm with coupled neuron.

A novel neuron model and its learning algorithm are presented. They provide a novel approach for speeding up convergence in the learning of layered neural networks and for training networks of neurons with a nondifferentiable output function by using the gradient descent method. The neuron is called a saturating linear coupled neuron (sl-CONE).