Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications.

The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.

Multiuser medical image encryption algorithm using phase-only CGH in the gyrator domain.

In this paper, a multiuser medical image encryption algorithm is proposed. The proposed algorithm utilizes polar decomposition, which enables multiuser features in the proposed algorithm. A computer-generated hologram (CGH) improves the security of the proposed algorithm in the gyrator domain. The phase-only CGH-based multiuser algorithm offers advantages such as storing a large amount of information in a compact space, resistance to counterfeiting, and enhanced security. The proposed method is validated with various statistical metrics, such as information entropy, mean squared error, correlation coefficient, histogram, and mesh plots. Results confirm that the proposed algorithm is secure and robust against potential attacks, such as plaintext attacks, iterative attacks, and contamination attacks. The proposed method has a large keyspace, which makes it very difficult to be breached in real-time with existing computational power.

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications.

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

Probiotic potential of gluten degrading Bacillus tequilensis AJG23 isolated from Indian traditional cereal-fermented foods as determined by Multiple Attribute Decision-Making analysis.

The present study reported the characterization of gluten hydrolyzing strains of Bacillus sp. from fermented cereal dough. The strains were characterized for probiotic as well as technological attributes. A total of 45 presumptive gluten degrading isolates were obtained on gliadin agar plate assay. Based on hemolytic and antibiotic susceptibility pattern, only six isolates were considered safe which also indicated gliadinase activity on zymography. All the six strains were able to resist the pH 2.0, 0.25% bile and also possessed ability to adhere to the organic solvents and mucin. The cell free supernatant of five strains exhibited antimicrobial activities against Gram-positive and Gram-negative pathogens. A more than 50% survival of the isolated strains was obtained at a salt concentration of 2%, phenol concentration of 0.1% and temperature upto 45 °C. All the strains exhibited antioxidant activities and biofilm forming ability. Furthermore, the ranking of strains based on probiotic as well as other functional attributes was determined using multidimensional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A matrix of multidimensional indicators was prepared using alternatives and criteria, the analysis indicated the strain Bacillus tequilensis AJG23 as the potential probiotic candidate based on all screening criteria. Further work still needs to be done about the protective role of the potential strain against gluten sensitivity using in vitro models.

Insights into the Glutamatergic and GABAergic Connections in the Conductance-Based Model of Subthalamic Nucleus in Hyperdirect Pathway.

Introduction: This study aimed to analyze the spiking patterns of subthalamic nucleus and globus pallidus coupling in hyperdirect pathway in healthy primates and in Parkinson's disease using a conductance-based model. The effect of calcium membrane potential has also been investigated. Materials and Methods: System of coupled differential equation arising from the conductance-based model has been simulated using ODE45 in MATLAB 7.14 to analyze the spiking patterns. Results: Analysis of spiking patterns suggests that subthalamic nucleus with synaptic input from globus pallidus in hyperdirect pathways is capable of showing two types of spiking pattern - irregular and rhythmic. Characterization of spiking patterns in healthy and Parkinson condition has been done based on their frequency, trend, and spiking rate. Results indicate that rhythmic patterns does not account for Parkinson's disease. Further, calcium membrane potential is an important parameter to target for identifying the cause of this disease. Conclusion: This work demonstrates that subthalamic nucleus and globus pallidus coupling in hyperdirect pathway can account for Parkinson's symptoms. However, the entire process of excitations and inhibition caused by glutamate and GABA receptors is limited by the timing of depolarization of the model. There is improvement in the correlation between healthy and Parkinson's patterns by increase in calcium membrane potential, however, for a limited time.

Modified plaintext attacks in a session for an optical cryptosystem based on DRPE with PFS.

In this paper, the security of an optical cryptosystem based on double random phase encoding (DRPE) with perfect forward secrecy (PFS) is analyzed for a particular session. In the cryptosystem, the PFS strategy is utilized to enhance the security and key management of the traditional DRPE scheme. Our analysis reveals that the use of PFS has certain advantages in the key management approach, but the method is still vulnerable against modified plaintext attacks when the attack is performed in the same session. Also, it is noted that the method is safe against conventional plaintext attacks, but it is vulnerable to the modified chosen and known plaintext attacks. The original plaintext can be easily retrieved with the proposed attack algorithms. Numerical simulation results are presented to validate the effectiveness of the proposed attack algorithms.

Sensitivity analysis of discharge patterns of subthalamic nucleus in the model of basal ganglia in Parkinson disease.

Parkinson disease alters the information patterns in movement related pathways in brain. Experimental results performed on rats show that the activity patterns changes from single spike activity to mixed burst mode in Parkinson disease. However the cause of this change in activity pattern is not yet completely understood. Subthalamic nucleus is one of the main nuclei involved in the origin of motor dysfunction in Parkinson disease. In this paper, a single compartment conductance based model is considered which focuses on subthalamic nucleus and synaptic input from globus pallidus (external). This model shows highly nonlinear behavior with respect to various intrinsic parameters. Behavior of model has been presented with the help of activity patterns generated in healthy and Parkinson condition. These patterns have been compared by calculating their correlation coefficient for different values of intrinsic parameters. Results display that the activity patterns are very sensitive to various intrinsic parameters and calcium shows some promising results which provide insights into the motor dysfunction.

Hodgkin-Huxley model based on ionic transport in axoplasmic fluid.

Hodgkin-Huxley model has been reframed to incorporate the physical parameters of fluid inside the axon. The reframed model comprises of set of partial differential equations containing the physical parameters: density, mass fraction of sodium, potassium and chlorine ions, longitudinal diffusivity of ions and rate of additions of ions along with the temperature. Obtained conduction velocity of 19.5m/sec at a temperature of 18.5 degree celcius and conduction velocity dependency on temperature within the range 5 to 25 degree celcius are two important results that strongly validate the proposed model. The behavior of all the physical parameters has been characterized with respect to the action potential. Action potential conduction velocity along with axoplasmic fluid viscosity has been characterized with respect to different temperatures. Longitudinal diffusivity of ions is also quantified.