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Automatic detection of brain tumors using genetic algorithms with multiple stages in magnetic resonance images

Annam Karthik, Kumar Sunil G., Babu Ashok P., Domala Narsaiah

Journal of Automation Mobile Robotics and Intelligent Systems

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2022

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Vol. 16, No. 4

36--43

EN

The field of biomedicine is still working on a solution to the challenge of diagnosing brain tumors, which is now one of the most significant challenges facing the profession. The possibility of an early diagnosis of brain cancer depends on the development of new technologies or instruments. Automated processes can be made possible thanks to the classification of different types of brain tumors by utilizing patented brain images. In addition, the proposed novel approach may be used to differentiate between different types of brain disorders and tumors, such as those that affect the brain. The input image must first undergo pre-processing before the tumor and other brain regions can be separated. Following this step, the images are separated into their respective colors and levels, and then the Gray Level Co-Occurrence and SURF extraction methods are used to determine which aspects of the photographs contain the most significant information. Through the use of genetic optimization, the recovered features are reduced in size. The cut-down features are utilized in conjunction with an advanced learning approach for the purposes of training and evaluating the tumor categorization. Alongside the conventional approach, the accuracy, inaccuracy, sensitivity, and specificity of the methodology under consideration are all assessed. The approach offers an accuracy rate greater than 90%, with an error rate of less than 2% for every kind of cancer. Last but not least, the specificity and sensitivity of each kind are higher than 90% and 50%, respectively. The usage of a genetic algorithm to support the approach is more efficient than using the other ways since the method that the genetic algorithm utilizes has greater accuracy as well as higher specificity.

2

Distributed sensor placement optimization for computer aided structural health monitoring

Ameduri Salvatore, Ciminello Monica, Dimino Ignazio, Concilio Antonio, Catignani Alfonso, Mancinelli Raimondo

Archive of Mechanical Engineering

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2019

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Vol. LXVI, nr 1

111--127

EN

An optimal sensor placement methodology is implemented and herein proposed for SHM model-assisted design and analysis purposes. The kernel of this approach analysis is a genetic-based algorithm providing the sensor network layout by optimizing the probability of detection (PoD) function while, in this preliminary phase, a classic strain energy approach is adopted as well established damage detection criteria. The layout of the sensor network is assessed with respect to its own capability of detection, parameterized through the PoD. A distributed fiber optic strain sensor is adopted in order to get dense information of the structural strain field. The overall methodology includes an original user-friendly graphical interface (GUI) that reduces the time-to-design costs needs. The proposed methodology is preliminarily validated for isotropic and anisotropic elements.

3

Optimization of a reactive fuzzy logic controller for a mobile robot using evolutionary algorithms

Meléndez A., Castillo O., Alanis A.

Journal of Automation Mobile Robotics and Intelligent Systems

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2010

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Vol. 4, No. 4

74-77

EN

This paper describes an evolutionary algorithm application for the optimization of a reactive fuzzy controller applied to mobile robot navigation. The evolutionary algorithm optimizes the fuzzy inference system and the position and number of the sensors on the robot, while trying to use the less power possible.

4

Optimal spoken dialog control in hands-free medical information systems

Sas J.

Journal of Medical Informatics & Technologies

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2009

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Vol. 13

113--120

EN

In the paper a method of optimal selection of utterances used as command entry-words for voice controlled application is presented. Voice controlled programs seem to be particularly useful in the area of medical informatics, where a physician interacts with a program by voice while operating the medical device or being involved in examinations requiring manual activities. The proposed method selects command words from sets of proposals defined for each command so as to minimize the overall probability of incorrect command recognition. First the entry-word dissimilarity matrix is calculated. The word dissimilarities are evaluated using HMM models consisting of appropriately trained acoustic models of the phonemes constituting words. The trained HMM is used as the sample utterance generator for the word. The artificially created utterance samples are then recognized by speech recognizers created for pairs of words. The estimation of correct recognition probability is used as the word dissimilarity measure. The word dissimilarities are then used to determine the average assessment of words selections that can be used as commands. Selection is created by choosing single word from sets of candidates defined for each command. Finally, suboptimal selection is found by using genetic algorithm. Experiments carried out prove that suboptimal selection of command entry-words can observably increase the accuracy of spoken commands recognition in many cases.

5

The use of auxetic materials in smart structures

Hadjigeorgiou E. P., Stavroulakis G. E.

Computational Methods in Science and Technology

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2004

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Vol. 10, nr 2

147-160

EN

This paper presents a study of the implications of using auxetic materials in the design of smart structures. By using auxetic materials as core and piezoelectric actuators as face layers to provide control forces, the problem of the shape control of sandwich beams is analyzed under loading conditions. The mechanical model is based on the shear deformable theory for beams and the linear theory of piezoelectricity. The numerical solution of the model is based on superconvergent (locking-free) finite elements for the beam theory, using Hamilton's principle. The optimal voltages of the piezo-actuators for shape control of a cantilever beams with classical and auxetic material are determined by using a genetic optimization procedure. Related numerical solutions of static problems demonstrate the role of auxetic material in the deformation, shape control and stress distribution of the beam and related two-dimensional composite elastic structures.