Cocluster structure analysis is a basic technique for revealing intrinsic structural information from cooccurrence data among objects and items, in which coclusters are composed of mutually familiar pairs of objects and items. In many real applications, it is also the case that we have not only cooccurrence information among objects and items but also intrinsic relation among items and other ingredients. For example, in food preference analysis, users&#x2019; preferences on foods should be found considering not only user-food cooccurrences but also the implicit relation among users and cooking ingredients. In this paper, two FCM-type fuzzy coclustering models, that is, FCCM and Fuzzy CoDoK, are extended for revealing intrinsic cocluster structures from three-mode cooccurrence data, where the aggregation degree of three elements in each cocluster is maximized through iterative updating of three types of fuzzy memberships for objects, items, and ingredients. The characteristic features of the proposed methods are demonstrated through a numerical experiment.

Katsuhiro Honda

Yurina Suzuki

Seiki Ubukata

Akira Notsu

FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK

Efficient traffic load balancing algorithm is very important to serve more mobile users in the cellular networks. This paper is based on mobility load balancing handoff algorithm using fuzzy logic. The rank of the serving and the neighboring Base Transceiver Stations (BTSs) are calculated every half second with the help of measurement report from the two-ray propagation model. This algorithm is able to balance load of the BTS by handing off some ongoing calls on BTS&#x2019;s edge of highly loaded BTS to move to overlapping underloaded BTS, such that the coverage area of loaded BTS virtually shrunk towards BTS center of a loaded sector. In case of low load scenarios, the coverage area of a BTS is presumed to be virtually widened to cover up to the partial serving area of neighboring BTS. This helps a highly loaded neighboring BTS or failed BTS due to power or transmission. Simulation shows that new call blocking and handoff blocking using the proposed algorithm are enhanced notably.

Solomon T. Girma

Abinet G. Abebe

Mobility Load Balancing in Cellular System with Multicriteria Handoff Algorithm

The correspondence between a vertex and its neighbors has an essential role in the structure of a graph. Type-2 soft sets are also based on the correspondence of primary parameters and underlying parameters. In this study, we present an application of type-2 soft sets in graph theory. We introduce vertex-neighbors based type-2 soft sets over <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.0498209pt" id="M1" height="8.68572pt" version="1.1" viewBox="-0.0498162 -8.6359 10.0819 8.68572" width="10.0819pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-89" d="M748 650H522L515 622L546 617C580 611 587 604 565 575C518 513 469 451 419 393C376 474 349 534 330 580C318 609 325 612 361 618L383 622L392 650H151L144 622C214 616 224 612 257 543L360 327C270 218 187 124 159 95C106 40 92 34 26 28L17 0H252L259 28L236 31C189 37 188 47 209 78C249 136 308 210 377 294L478 79C494 44 487 37 449 32L418 28L409 0H673L680 28C596 34 591 39 554 116L436 361C526 469 574 521 604 553C659 612 669 614 739 622L748 650Z"/></g></svg> (set of all vertices of a graph) and <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.3238297pt" id="M2" height="9.49473pt" version="1.1" viewBox="-0.0498162 -9.1709 10.8779 9.49473" width="10.8779pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g199-6" d="M414 683C381 698 350 699 311 699C179 699 95 631 95 552C95 463 191 406 337 398C338 393 340 388 344 382V380C189 341 105 257 105 156C105 39 220 -21 348 -21C426 -21 522 5 582 62C613 91 635 128 635 173C635 246 589 289 490 289C377 289 308 231 284 116L333 106C358 198 403 234 462 234C487 234 521 213 521 170C521 86 441 34 353 34C281 34 239 75 239 135C239 220 299 298 396 331C411 324 434 317 459 317C508 317 531 335 531 358S502 388 451 395V398C651 419 791 527 791 607C791 660 748 687 661 687C527 687 356 582 334 453C253 458 186 483 186 549C186 606 239 644 323 644C355 644 373 643 400 635L414 683ZM456 455C480 542 580 632 655 632C686 632 700 624 700 601C700 541 595 472 458 453L456 455Z"/></g></svg> (set of all edges of a graph). Moreover, we introduce some type-2 soft operations in graphs by presenting several examples to demonstrate these new concepts. Finally, we describe an application of type-2 soft graphs in communication networks and present procedure as an algorithm.

Khizar Hayat

Muhammad Irfan Ali

Bing-Yuan Cao

Xiao-Peng Yang

A New Type-2 Soft Set: Type-2 Soft Graphs and Their Applications

The main purpose of this paper is to introduce the notion of <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063904pt" id="M2" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-110"/></g></svg>-polar <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063999pt" id="M3" height="9.49473pt" version="1.1" viewBox="-0.0498162 -9.28833 6.83278 9.49473" width="6.83278pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-105" d="M499 88L487 113C459 86 422 65 413 65C403 65 403 74 409 98L461 318C487 426 460 448 431 448C408 448 383 441 352 424C305 398 223 344 157 257H155L243 674C249 702 249 712 240 712C227 712 170 680 87 673L82 648H117C155 648 162 644 150 590L23 -4L30 -12C55 -4 81 3 105 8C113 53 131 150 143 187C199 281 323 387 372 387C390 387 393 369 380 311L328 86C313 19 319 -12 347 -12C379 -12 442 24 499 88Z"/></g></svg>-morphism on <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063904pt" id="M4" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-110"/></g></svg>-polar fuzzy graphs. The action of <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063904pt" id="M5" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-110"/></g></svg>-polar <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063999pt" id="M6" height="9.49473pt" version="1.1" viewBox="-0.0498162 -9.28833 6.83278 9.49473" width="6.83278pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-105"/></g></svg>-morphism on <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063904pt" id="M7" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-110"/></g></svg>-polar fuzzy graphs is studied. Some elegant theorems on weak and coweak isomorphism are obtained. Also, some properties of highly irregular, edge regular, and totally edge regular <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063904pt" id="M8" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-110"/></g></svg>-polar fuzzy graphs are studied.

Ch. Ramprasad

P. L. N. Varma

S. Satyanarayana

N. Srinivasarao

Morphism of <svg  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"   style="vertical-align:-0.2063904pt" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-110" d="M766 88L752 113C719 83 690 64 681 64C674 64 672 74 679 103L724 292C758 436 724 448 701 448C680 448 666 442 639 429C594 407 514 350 441 252H439L447 289C476 423 450 448 419 448C398 448 379 441 355 427C307 400 234 344 162 249H160L180 324C203 409 197 448 170 448C144 448 82 413 23 349L35 321C57 343 96 374 108 374C115 374 117 371 111 341C87 227 57 112 24 -6L32 -12C53 -4 81 4 108 6C119 68 134 128 149 171C177 229 309 383 364 383C387 383 388 355 373 282C354 190 330 92 303 -6L309 -12C332 -4 356 3 386 6C396 63 411 122 424 171C458 236 590 383 642 383C658 383 664 369 652 315L603 91C587 20 593 -12 619 -12C642 -12 708 23 766 88Z" /></g></svg>-Polar Fuzzy Graph

Computational intelligence and computer science rely on graph theory to solve combinatorial problems. Normal product and tensor product of an <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063904pt" id="M2" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-110"/></g></svg>-polar fuzzy graph have been introduced in this article. Degrees of vertices in various product graphs, like Cartesian product, composition, tensor product, and normal product, have been computed. Complement and <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.42943pt" id="M3" height="9.39034pt" version="1.1" viewBox="-0.0498162 -5.96091 7.34167 9.39034" width="7.34167pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-234" d="M538 96L524 119C492 88 454 63 446 63C439 63 434 70 440 101C463 223 491 341 518 448H508L433 422L401 276C355 192 240 56 188 56C163 56 154 89 163 133C184 233 207 338 235 443L230 448L152 424L58 17C40 -60 23 -143 23 -185C23 -241 42 -261 63 -261C92 -261 117 -241 125 -227L124 -221C109 -209 89 -165 89 -103C89 -55 96 -13 105 26H107C121 -3 134 -12 151 -12C172 -12 194 -5 221 16C279 61 330 124 384 194H386C381 159 377 141 368 105C343 3 363 -12 383 -12C416 -12 486 35 538 96Z"/></g></svg>-complement of an <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063904pt" id="M4" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-110"/></g></svg>-polar fuzzy graph are defined and some properties are studied. An application of an <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.2063904pt" id="M5" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-110"/></g></svg>-polar fuzzy graph is also presented in this article.

Vertex Degrees and Isomorphic Properties in Complement of an <svg  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"   style="vertical-align:-0.2063904pt" height="6.1673pt" version="1.1" viewBox="-0.0498162 -5.96091 10.3951 6.1673" width="10.3951pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-110" d="M766 88L752 113C719 83 690 64 681 64C674 64 672 74 679 103L724 292C758 436 724 448 701 448C680 448 666 442 639 429C594 407 514 350 441 252H439L447 289C476 423 450 448 419 448C398 448 379 441 355 427C307 400 234 344 162 249H160L180 324C203 409 197 448 170 448C144 448 82 413 23 349L35 321C57 343 96 374 108 374C115 374 117 371 111 341C87 227 57 112 24 -6L32 -12C53 -4 81 4 108 6C119 68 134 128 149 171C177 229 309 383 364 383C387 383 388 355 373 282C354 190 330 92 303 -6L309 -12C332 -4 356 3 386 6C396 63 411 122 424 171C458 236 590 383 642 383C658 383 664 369 652 315L603 91C587 20 593 -12 619 -12C642 -12 708 23 766 88Z" /></g></svg>-Polar Fuzzy Graph

There has been increasing interest in improving the mobility of ground vehicles. The interest is greater in predicting the mobility for military vehicles. In this paper, authors review various definitions of mobility. Based on this review, a new definition of mobility called fuzzy mobility is given. An algorithm for fuzzy mobility assessment is described with the help of fuzzy rules. The simulation is carried out and its implementation, testing, and validation strategies are discussed.

Aby K. George

Harpreet Singh

Macam S. Dattathreya

Thomas J. Meitzler

A Fuzzy Simulation Model for Military Vehicle Mobility Assessment

In areas of medical diagnosis and decision-making, several uncertainty and ambiguity shrouded situations are most often imposed. In this regard, one may well assume that intuitionistic fuzzy sets (IFS) should stand as a potent technique useful for demystifying associated with the real healthcare decision-making situations. To this end, we are developing a prototype model helpful for detecting the patients risk degree in Intensive Care Unit (ICU). Based on the intuitionistic fuzzy sets, dubbed Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS), the shown work has its origins in the Modified Early Warning Score (MEWS) standard. It is worth noting that the proposed prototype effectiveness validation is associated through a real case study test at the Polyclinic ESSALEMA cited in Sfax, Tunisia. This paper does actually provide some practical initial results concerning the system as carried out in real life situations. Indeed, the proposed system turns out to prove that the MIFEDSS does actually display an imposing capability for an established handily ICU related uncertainty issues. The performance of the prototypes is compared with the MEWS standard which exposed that the IFS application appears to perform highly better in deferring accuracy than the expert MEWS score with higher degrees of sensitivity and specificity being recorded.

Hanen Jemal

Zied Kechaou

Mounir Ben Ayed

Enhanced Decision Support Systems in Intensive Care Unit Based on Intuitionistic Fuzzy Sets

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley&#x2019;s segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms.

Min Chen

Simone A. Ludwig

Color Image Segmentation Using Fuzzy C-Regression Model

In this work an extension of the Fuzzy Possibilistic C-Means (FPCM) algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM) algorithm to observe if the proposed approach has better performance than this algorithm.

Elid Rubio

Oscar Castillo

Fevrier Valdez

Patricia Melin

Claudia I. Gonzalez

Gabriela Martinez

An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

A spherical wheel robot or Ballbot&#x2014;a robot that balances on an actuated spherical ball&#x2014;is a new and recent type of robot in the popular area of mobile robotics. This paper focuses on the modeling and control of such a robot. We apply the Lagrangian method to derive the governing dynamic equations of the system. We also describe a novel Fuzzy Sliding Mode Controller (FSMC) implemented to control a spherical wheel mobile robot. The nonlinear nature of the equations makes the controller nontrivial. We compare the performance of four different fuzzy controllers: (a) regulation with one signal, (b) regulation and position control with one signal, (c) regulation and position control with two signals, and (d) FSMC for regulation and position control with two signals. The system is evaluated in a realistic simulation and the robot parameters are chosen based on a LEGO platform, so the designed controllers have the ability to be implemented on real hardware.

Hamed Navabi

Soroush Sadeghnejad

Sepehr Ramezani

Jacky Baltes

Position Control of the Single Spherical Wheel Mobile Robot by Using the Fuzzy Sliding Mode Controller

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<p><em>Advances in Fuzzy Systems</em> provides an international forum for original research articles in the theory and applications of fuzzy subsets and systems.</p>


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<p>Chief Editor, Professor Melin, is a professor at the Tijuana Institute of Technology. Her research interests include modular neural networks, type-2 fuzzy logic, pattern recognition, fuzzy control, neuro-fuzzy and genetic-fuzzy hybrid approaches.</p>


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Advances in Fuzzy Systems - Table of contents 2017

title

Advances in Fuzzy Systems provides an international forum for original research articles in the theory and applications of fuzzy subsets and systems.

Advances in Fuzzy Systems