School of Mathematics and Computer Applications

Both parallel and distributed network environment systems play a vital role in the improvement of high performance computing. The primary concern when analyzing these systems is multiprocessor task scheduling. This paper addresses the problem of efficient multiprocessor task scheduling. A multiprocessor task scheduling problem is represented as directed acyclic task graph (DAG), for execution on multiprocessors with communication costs. In this paper we have investigated the effectiveness of a proposed paradigm based on genetic algorithms (GAs). GAs is a class of robust stochastic search algorithms for various combinatorial optimization problems. We have designed a GA based encoding mechanism that uses multi-chromosome encoding scheme. The implementation of the technique is simple. The performance of the designed algorithm has been tested on a variety of multiprocessor systems both heterogeneous as well as homogeneous.

Cloud Computing is a term which is generally used in reference to Internet. The whole Internet can be viewed as a cloud. The cloud is quickly changing a worldwide network of computers into the largest single, "virtual" computer in the world. This includes sharing of resources which increases the load on single machine. Thus the overall performance degrades and this problem is named as load balancing. Load Balancing is one of the challenging issues now a day. To overcome from this problem be reassign the load by implementing various algorithms introduced for this problem. In this paper we study and compare various algorithms, techniques used to solve the problem of Load Balancing and Distributed Load Balancing.

An underwater ad-hoc sensor network differs from terrestrial network in terms of energy consumption, communication and topology. An acoustic communication is identified as energy efficient way of communication in such networks. Further a multi-hop acoustic communication from bottom of the water to the surface adds many folds to save sensors' energy. To avoid battery recharging or replacement a better topology and improved communication method needs to be developed. In order to exploit multi-hoping advantages and to save energy we have proposed earlier an Under Water Node-Density Based Clustering Sensor Network Protocol (UWDBCSN).

The Underwater Acoustic Sensor Networks (UWASN) consists of sensors that are deployed underwater for gathering information for the unexplored parts of oceans or rivers. UWASN consists of variable number of floating and anchored sensors, sink and vehicles that are deployed over an area to be explored. The characteristics of UWASN are mainly node mobility for floating, capacity for data collection and recording and autonomous vehicles which are battery operable. The communication is possible among underwater devices through optical waves, radio waves, electromagnetic and acoustics. Out of these, acoustics communication is best suited as it can carry digital information through underwater channel and can travel to longer distances. The communication can be classified in two parts: Single and multi hoping. But in underwater we use multi-hop communication for sending data from end nodes to sink nodes. The various challenges to UWASN are limited bandwidth, multipath fading, limited battery, limited data capacity and delay in propagation. Hence, in this paper we have focussed on various issues and challenges in underwater wireless sensor networks for acoustic communications.

An underwater ad-hoc sensor network differs from terrestrial network in terms of energy consumption, communication and topology. An acoustic communication is identified as energy efficient way of communication in such networks. Further a multi-hop acoustic communication from bottom of the water to the surface adds many folds to save sensors’ energy. To avoid battery recharging or replacement a better topology and improved communication method needs to be developed. In order to exploit multi-hoping advantages and to save energy we have proposed earlier an Under Water Node-Density Based Clustering Sensor Network Protocol (UWDBCSN). In this paper we will give a glimpse of that protocol and discuss some consequences of Acoustic Communication like spreading loss, absorption loss and path loss in the protocol.

Precision agriculture system is an arising idea that refers to overseeing farms utilizing current information and communication technologies to improve the quantity and quality of yields while advancing the human work required. The automation requires the assortment of information given by the sensors such as soil, water, light, humidity, temperature for additional information to furnish the operator with exact data to acquire excellent yield to farmers. In this work, a study is proposed that incorporates all common state-of-the-art approaches for precision agriculture use. Technologies like the Internet of Things (IoT) for data collection, machine Learning for crop damage prediction, and deep learning for crop disease detection is used. The data collection using IoT is responsible for the measure of moisture levels for smart irrigation, n, p, k estimations of fertilizers for best yield development. For crop damage prediction, various algorithms like Random Forest (RF), Light gradient boosting machine (LGBM), XGBoost (XGB), Decision Tree (DT) and K Nearest Neighbor (KNN) are used. Subsequently, Pre-Trained Convolutional Neural Network (CNN) models such as VGG16, Resnet50, and DenseNet121 are also trained to check if the crop was tainted with some illness or not.

Most of the traditional histogram-based thresholding techniques are effective for bi-level thresholding and unable to consider spatial contextual information of the image for selecting optimal threshold. In this article a novel thresholding technique is presented by proposing an energy function to generate the energy curve of an image by taking into an account the spatial contextual information of the image. The behavior of this energy curve is very much similar to the histogram of the image. To incorporate spatial contextual information of the image for threshold selection process, this energy curve is used as an input of our technique instead of histogram. Moreover, to mitigate multilevel thresholding problem the properties of genetic algorithm are exploited. The proposed algorithm is evaluated on the number of different types of images using a validity measure. The results of the proposed technique are compared with those obtained by using histogram of the image and also with an existing genetic algorithm based context sensitive technique. The comparisons confirmed the effectiveness of the proposed technique.

In the present article, the notion of αH-ψH-multivalued contractive type mappings is introduced and some fixed point results in complete metric spaces are studied. These theorems generalize Nadler’s (Multivalued contraction mappings, Pac. J. Math., 30, 475–488, 1969) and Suzuki-Kikkawa's (Three fixed point theorems for generalized contractions with constants in complete metric spaces, Nonlinear Anal., 69, 2942–2949, 2008) results that exist in the literature. The effectiveness of the obtained results has been verified with the help of some comparative examples. Moreover, a homotopy result has been presented as an application.

In the present work, the concept of F-generalized contractive type mappings by using C-class functions is introduced, and some common fixed point results for weakly isotone increasing set-valued mappings in the setting of ordered partial metric spaces are studied. These results improve and generalize various results existing in the literature. The effectiveness of the obtained results is verified with the help of some comparative examples.

In the present work, the concept of F-generalized contractive type mappings by using C-class functions is introduced, and some common fixed point results for weakly isotone increasing set-valued mappings in the setting of ordered partial metric spaces are studied. These results improve and generalize various results existing in the literature. The effectiveness of the obtained results is verified with the help of some comparative examples.

In the present paper, we extend the concept of contraction in a new manner by introducing D-contraction defined on a family F of bounded functions. We also introduce a new notion of a fixed function on a metric space. Some fixed function theorems along with illustrative examples and application are also given to verify the effectiveness of our results.

In the present article, the concept of expansion is extended in a refined manner by introducing P-expansion defined on a family F of bounded functions. Some fixed function theorems using expansive mappings in complete metric spaces are investigated. These results improve and generalize various results existing in literature. The authenticity of obtained results is verified with the help of some comparative examples. In addition, an application has also been presented which is based on the best approximation of dose distribution for a number of patients (at a same time) getting Tomotherapy.

In the present paper, the concept of contraction has been extended in a refined manner by introducing $\mathfrak{D}$-Contraction defined on a family $\mathfrak{F}$ of bounded functions. Also, a new notion of fixed function has been introduced for a metric space. Some fixed function theorems along with illustrative examples have also been given to verify the effectiveness of our results. In addition, an application to medical science has also been presented. This application is based on best approximation of treatment plan for tumor patients getting intensity modulated radiation therapy(IMRT). In this technique, a proper DDC matrix truncation has been used that significantly improves accuracy of results. In 2013, Z. Tian \textit{et al.} presented a fluence map optimization(FMO) model for dose calculation by splitting the DDC matrix into two components on the basis of a threshold intensity value. Following this concept, a sequence of functions can be constructed through the presented ...

Type-2 intuitionistic fuzzy set (T2IFS) is a powerful and important extension of the classical fuzzy set, intuitionistic fuzzy set to measure the vagueness and uncertainty. In a practical decision-making process, there always occurs an inter-relationship among the multi-input arguments. To deal with this point, the motivation of the present paper is to develop some new interval type-2 (IT2) intuitionistic fuzzy aggregation operators which can consider the multi interaction between the input argument. To achieve it, we define a symmetric triangular interval T2IFS (TIT2IFS), its operations, Hamy mean (HM) operator to aggregate the preference of the symmetric TIT2IFS and then shows its applicability through a multi-criteria decision making (MCDM). Several enviable properties and particular cases together with following different parameter values of this operator are calculated in detail. At last a numerical illustration is to given to exemplify the practicability of the proposed techni...

Background: Chemical straightening has increased in popularity because of its durability over thermal straightening and because it gives the hair a natural smooth, shiny look, more styling options for a longer time. The aim of this study was to evaluate the degree of damage that occur to the hair after chemical straightener treatment by assessing the change in hair porosity and hair cysteic acid content before and after straightener treatment. Methods: Forty hair samples were collected from female volunteers; each sample was divided into 2 groups; group A (before straightening) and group B (after straightening). Hair samples were washed then blow dried after that hair straightener was applied for 30 min then flat iron was done, hair samples were washed and blow dried. Each group was subjected to test their porosity and Fourier transform infrared spectroscopy examination to assess cysteic acid content. Results: Hair porosity showed significant increase after straitening. Cysteic acid and disulfide content showed insignificant difference between both groups. Conclusion: Hair straighteners may cause great damage to hair internal structures. So, proper choice of the material used and assessing the hair characters before straightening is necessary.

The objective of this work is to present a triangular interval type-2 (TIT2) intuitionistic fuzzy sets and their corresponding aggregation operators, namely, TIT2 intuitionistic fuzzy weighted averaging, TIT2 intuitionistic fuzzy ordered weighted averaging and TIT2 intuitionistic fuzzy hybrid averaging based on Frank norm operation laws. Furthermore, based on these operators, an approach to multi-criteria decision-making, in which assessments are in the form of TIT2 intuitionistic fuzzy numbers has been developed. A practical example to illustrate the decision-making process has been presented and compared their results with the existing operator results.

In this study, a new composite algorithm with the help of the finite difference and the modified cubic trigonometric B-spline differential quadrature method is developed. The developed method was applied to two-dimensional coupled Burgers’ equation with initial and Dirichlet boundary conditions for computational modeling. The established algorithm is better than the traditional differential quadrature algorithm proposed in literature due to more smoothness of cubic trigonometric B-spline functions. In the development of the algorithm, the first step is semidiscretization in time with the forward finite difference method. Furthermore, the obtained system is fully discretized by the modified cubic trigonometric B-spline differential quadrature method. Finally, we obtain coupled Lyapunov systems of linear equations, which are analyzed by the MATLAB solver for the system. Moreover, comparative study of these solutions with the numerical and exact solutions which are appeared in the lite...

A bi-objective fuzzy transportation problem with the objectives to minimize the total fuzzy cost and fuzzy time of transportation without according priorities to them is considered. To the best of our knowledge, there is no method in the literature to find efficient solutions of the bi-objective transportation problem under uncertainty. In this paper, a bi-objective transportation problem in an uncertain environment has been formulated. An algorithm has been proposed to find efficient solutions of the bi-objective transportation problem under uncertainty. The proposed algorithm avoids the degeneracy and gives the optimal solution faster than other existing algorithms for the given uncertain transportation problem.

This work analyze singularly perturbed convection-diffusion-reaction (CDR) models with two parameters and variable coefficients by developing a mesh-free scheme based on local radial basis function-finite difference (LRBF-FD) approximation. In the evolvement of the scheme, time derivative is discretized by forward finite difference. After that, LRBF-FD approximation is used for spatial discretization, and we obtained a system of linear equations. Then, the obtained linear system is solved by LU decomposition method in MATLAB. For numerical simulation, four singularly perturbed models are pondered to check the efficiency and chastity of the proposed scheme.

In the present work, the concept of F-generalized contractive type mappings by using C-class functions is introduced, and some common fixed point results for weakly isotone increasing set-valued mappings in the setting of ordered partial metric spaces are studied. These results improve and generalize various results existing in the literature. The effectiveness of the obtained results is verified with the help of some comparative examples.