A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 &#xD7; 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

Oussama Ait Sahed

Kamel Kara

Mohamed Laid Hadjili

Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

This paper proposes a new technique for hiding secret messages in ordinary English text. The proposed technique exploits the redundancies existing in some English language constructs. Redundancies result from the flexibility in maneuvering certain statement constituents without altering the statement meaning or correctness. For example, one can say &#x201c;she went to sleep, because she was tired&#x201d; or &#x201c;Because she was tired, she went to sleep.&#x201d; The paper provides a number of such transformations that can be applied concurrently, while keeping the overall meaning and grammar intact. The proposed data hiding technique is blind since the receiver does not keep a copy of the original uncoded text (cover). Moreover, it can hide more than three bits per statement, which is higher than that achieved in the prior work. A secret key that is a function of the various transformations used is proposed to protect the confidentiality of the hidden message. Our security analysis shows that even if the attacker knows how the transforms are employed, the secret key provides enough security to protect the confidentiality of the hidden message. Moreover, we show that the proposed transformations do not affect the inconspicuousness of the transformed statements, and thus unlikely to draw suspicion.

Salma Banawan

Ibrahim Kamel

Hiding Information in Reversible English Transforms for a Blind Receiver

In real world, many optimization problems are dynamic, which means that their
model elements vary with time. These problems have received increasing
attention over time, especially from the viewpoint of metaheuristics methods. 
In this context, experimentation is a crucial task because of the
stochastic nature of both algorithms and problems. Currently, there are several
technologies whose methods, problems, and performance measures can be implemented. 
However, in most of them, certain features that make the experimentation process easy are
not present. Examples of such features are the statistical analysis of the results and a graphical
user interface (GUI) that allows an easy management of the experimentation process. Bearing in
mind these limitations, in the present work, we present DynOptLab, a software tool for experimental
analysis in dynamic environments. DynOptLab has two main components: (1) an
object-oriented framework to facilitate the implementation of new proposals and
(2) a graphical user interface for the experiment management and the statistical
analysis of the results. With the aim of verifying the benefits of DynOptLab&#x2019;s main features, a
typical case study on experimentation in dynamic environments was carried out.

Pavel Novoa-Hern&#xe1;ndez

Carlos Cruz Corona

David A. Pelta

A Software Tool for Assisting Experimentation in Dynamic Environments

A new framework intended for representing and segmenting multidimensional datasets resulting in low spatial complexity requirements and with appropriate access to their contained information is described. Two steps are going to be taken in account. The first step is to specify (<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.3625803pt" id="M2" height="9.06134pt" version="1.1" viewBox="-0.0498162 -8.69876 27.4925 9.06134" width="27.4925pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-111"/></g><g transform="matrix(.0135,0,0,-0.0135,9.829,0)"><path id="g117-33" d="M535 230V280H52V230H535Z"/></g><g transform="matrix(.0135,0,0,-0.0135,20.81,0)"><path id="g113-50" d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"/></g></svg>)D hypervoxelizations, <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-1.16493pt" id="M3" height="9.86369pt" version="1.1" viewBox="-0.0498162 -8.69876 29.0065 9.86369" width="29.0065pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-111"/></g><g transform="matrix(.0135,0,0,-0.0135,10.586,0)"><path id="g117-94" d="M531 285V335L57 550V494L473 310L57 127V71L531 285ZM531 -40V10H57V-40H531Z"/></g><g transform="matrix(.0135,0,0,-0.0135,22.324,0)"><path id="g113-51" d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z"/></g></svg>, as Orthogonal Polytopes whose <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2129903pt" id="M4" height="6.42315pt" version="1.1" viewBox="-0.0498162 -6.21016 6.92631 6.42315" width="6.92631pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-111"/></g></svg>th dimension corresponds to color intensity. Then, the <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2129903pt" id="M5" height="6.42315pt" version="1.1" viewBox="-0.0498162 -6.21016 6.92631 6.42315" width="6.92631pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-111"/></g></svg>D representation is concisely expressed via the Extreme Vertices Model in the <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2129903pt" id="M6" height="6.42315pt" version="1.1" viewBox="-0.0498162 -6.21016 6.92631 6.42315" width="6.92631pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-111"/></g></svg>-Dimensional Space (<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2129903pt" id="M7" height="6.42315pt" version="1.1" viewBox="-0.0498162 -6.21016 6.92631 6.42315" width="6.92631pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-111"/></g></svg>D-EVM). Some examples are presented, which, under our methodology, have storing requirements minor than those demanded by their original hypervoxelizations. In the second step, 1-Dimensional Kohonen Networks (1D-KNs) are applied in order to segment datasets taking in account their geometrical and topological properties providing a non-supervised way to compact even more the proposed <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2129903pt" id="M8" height="6.42315pt" version="1.1" viewBox="-0.0498162 -6.21016 6.92631 6.42315" width="6.92631pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-111"/></g></svg>-Dimensional representations. The application of our framework shares compression ratios, for our set of study cases, in the range 5.6496 to 32.4311. Summarizing, the contribution combines the power of the <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2129903pt" id="M9" height="6.42315pt" version="1.1" viewBox="-0.0498162 -6.21016 6.92631 6.42315" width="6.92631pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-111"/></g></svg>D-EVM and 1D-KNs by producing very concise datasets&#x2019; representations. We argue that the new representations also provide appropriate segmentations by introducing some error functions such that our 1D-KNs classifications are compared against classifications based only in color intensities. Along the work, main properties and algorithms behind the <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2129903pt" id="M10" height="6.42315pt" version="1.1" viewBox="-0.0498162 -6.21016 6.92631 6.42315" width="6.92631pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><use xlink:href="#g113-111"/></g></svg>D-EVM are introduced for the purpose of interrogating the final representations in such a way that it efficiently obtains useful geometrical and topological information.

Ricardo P&#xe9;rez-Aguila

Ricardo Ruiz-Rodr&#xed;guez

Concise and Accessible Representations for Multidimensional Datasets: Introducing a Framework Based on the <svg  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2129903pt" height="6.42315pt" version="1.1" viewBox="-0.0498162 -6.21016 6.92631 6.42315" width="6.92631pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path id="g113-111" d="M495 86L479 114C446 82 419 66 409 66C401 66 401 72 406 97C420 166 436 231 453 297C489 435 454 448 428 448C406 448 384 439 354 422C305 394 222 327 161 247H159L183 345C200 415 194 448 173 448C143 448 82 410 23 351L38 325C64 349 95 371 105 371C111 371 116 365 109 336L25 -4L31 -12C50 -4 77 3 107 9C119 69 132 122 145 168C197 254 321 381 370 381C387 381 393 374 378 305L329 95C309 17 320 -12 345 -12C372 -12 430 19 495 86Z" /><glyph.data ascent="3473" descent="-2876" horiz-adv-x="502" vert-adv-y="502" /></g></svg>D-EVM and Kohonen Networks

The paper is about the application of mini minibatch stochastic gradient descent (SGD) based learning applied to Multilayer Perceptron in the domain of isolated Devanagari handwritten character/numeral recognition. This technique reduces the variance in the estimate of the gradient and often makes better use of the hierarchical memory organization in modern computers. <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.04981995pt" id="M1" height="9.04777pt" version="1.1" viewBox="-0.0498162 -8.99795 14.1474 9.04777" width="14.1474pt"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path id="g113-77" d="M541 160L512 170C485 116 462 88 439 67C411 41 376 35 318 35C272 35 238 37 224 48C207 61 205 85 212 121L290 533C305 611 308 615 391 622L397 650H139L133 622C217 615 221 612 206 533L126 118C111 41 103 34 23 26L17 0H474C489 31 528 124 541 160Z"/></g><g transform="matrix(.0135,0,0,-0.0135,7.488,0)"><path id="g113-51" d="M412 140C382 77 369 73 315 73H129L270 222C362 320 402 379 402 466C402 571 322 635 234 635C177 635 130 609 99 576L42 495L64 475C90 514 133 568 201 568C274 568 318 519 318 435C318 349 255 267 193 193C144 135 87 78 32 23V0H405C417 45 427 89 440 131L412 140Z"/></g></svg>-weight decay is added on minibatch SGD to avoid overfitting. The experiments are conducted firstly on the direct pixel intensity values as features. After that, the experiments are performed on the proposed flexible zone based gradient feature extraction algorithm. The results are promising on most of the standard dataset of Devanagari characters/numerals.

Pratibha Singh

Ajay Verma

Narendra S. Chaudhari

On the Performance Improvement of Devanagari Handwritten Character Recognition

Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.

Oliver Kramer

Cascade Support Vector Machines with Dimensionality Reduction

We put forward architecture of a framework for integration of data from moving objects related to urban transportation network. Most of this research refers to the GPS outdoor geolocation technology and uses distributed cloud infrastructure with big data NoSQL database. A network of intelligent mobile sensors, distributed on urban network, produces congestion traffic patterns. Congestion predictions are based on extended simulation model. This model provides traffic indicators calculations, which fuse with the GPS data for allowing estimation of traffic states across the whole network. The discovery process of congestion patterns uses semantic trajectories metamodel given in our previous works. The challenge of the proposed solution is to store patterns of traffic, which aims to ensure the surveillance and intelligent real-time control network to reduce congestion and avoid its consequences. The fusion of real-time data from GPS-enabled smartphones integrated with those provided by existing traffic systems improves traffic congestion knowledge, as well as generating new information for a soft operational control and providing intelligent added value for transportation systems deployment.

A. Boulmakoul

L. Karim

M. Mandar

A. Idri

A. Daissaoui

Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing

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<p><em>Applied Computational Intelligence and Soft Computing</em> provides a forum for research that connects the disciplines of computer science, engineering, and mathematics using the technologies of computational intelligence and soft computing.</p>


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Applied Computational Intelligence and Soft Computing provides a forum for research that connects the disciplines of computer science, engineering, and mathematics using the technologies of computational intelligence and soft computing.

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Applied Computational Intelligence and Soft Computing

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Applied Computational Intelligence and Soft Computing