Ayah Salama Khaskia

The question of how an individual firm's social and environmental performance impacts its firm risk has not been examined in any empirical UK research. Does a company that strives to attain good environmental performance decrease its market risk or is environmental performance just a disadvantageous cost that increases such risk levels for these firms? Answers to this question have important implications for the management of companies and the investment decisions of individuals and institutions. The purpose of this paper is to examine the relationship between corporate environmental performance and firm risk in the British context. Using the largest dataset assembled so far, with community and environmental responsibility (CER) rankings for all rated UK companies between 1994 and 2006, we show that a company's environmental performance is inversely related to its systematic financial risk. However, an increase of 1.0 in the CER score is associated with only a 0.028 reduction in its β.

This paper presents an approach for underdetermined blind source separation in the case of additive Gaussian white noise and pink noise. Likewise, the proposed approach is applicable in the case of separating I + 3 sources from I mixtures with additive two kinds of noises. This situation is more challenging and suitable to practical real world problems. Moreover, unlike to some conventional approaches, the sparsity conditions are not imposed. Firstly, the mixing matrix is estimated based on an algorithm that combines short time Fourier transform and rough-fuzzy clustering. Then, the mixed signals are normalized and the source signals are recovered using modified Gradient descent Local Hierarchical Alternating Least Squares Algorithm exploiting the mixing matrix obtained from the previous step as an input and initialized by multiplicative algorithm for matrix factorization based on alpha divergence. The experiments and simulation results show that the proposed approach can separate I + 3 source signals from I mixed signals, and it has superior evaluation performance compared to some conventional approaches.

In this paper we introduce the notion of generalized intuitionistic fuzzy ideals which is considered as a generalization of fuzzy intuitionistic ideals studies in [6], the important generalized intuitionistic fuzzy ideals has been given. The concept of generalized intuitionistic fuzzy local function is also introduced for a generalized intuitionistic fuzzy topological space. These concepts are discussed with a view to find new generalized intuitionistic fuzzy topology from the original one in [5, 7]. The basic structure, especially a basis for such generated generalized intuitionistic fuzzy topologies and several relations between different generalized intuitionistic fuzzy ideals and generalized intuitionistic fuzzy topologies are also studied here.

The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. Technological developments like edge computing, fog computing, Internet of Things (IoT), and Big Data have gained importance due to their robustness and ability to provide diverse response characteristics based on target application. In this paper, we present a novel Health-Fog framework universal system to automatically assist the early diagnosis, treatment, and preventive of people with COVID-19 in an efficient manner. Achieving an empirical of the proposed framework which mix between deep learning and Neutrosophic classifiers in the task of classifying COVID-19. There are some proposed applications based on the proposed COVID-X framework such as smart mask, smart medical suit, safe spacer, and Medical Mobile Learning (MML) will be presented. Computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates

: Paper aims to use the programming codes in calculating the values of neutrosophic grades and their representation in proving the certainty and uncertainty associated with the data of navigational projects development in the Suez Canal, Egypt. Added to, we reach a more descriptive of the data in terms of certainty and uncertainty, and that is through the neutrosophic representation of both the total revenue and the revenues of the Suez Canal from the transit carriers and ships. Finally, we will present a study of the decision-making process regarding the better investment in the Suez Canal. Is it investing in the oil tankers or investing in cargo ships, as this is done based on neutrosophic data. This will be done by studying optimistic, pessimistic, and remorse entrances to the neutrosophic data, to see which oil tankers or cargo ships offer better returns to the Canal.

In this paper, we present the Neutrosophic time series by studying the classical time series within the framework of the Neutrosophic logic.(Logic established by the American philosopher and mathematician Florentin Smarandache presented it as a generalization of fuzzy logic, especially intuitionistic fuzzy logic). As an extension of this, AA Salama presented the theory of Neutrosophic crisp sets as a generalization of crisp sets theory. This study enables us to deal with all the time series values whether it is specified or not specified, we present the linear model for the Neutrosophic time series, and we test the significant of its coefficient based on Student's distribution. We present an example in which we pave the Neutrosophic time series according to the linear model, test the significant of its coefficient, and show how to deal with the unspecified values of the time series. Then we present the logarithmic model of the …

Cardiotocography is a medical device that monitors fetal heart rate and the uterine contraction during the period of pregnancy. It is used to diagnose and classify a fetus state by doctors who have challenges of uncertainty in data. The Rough Neural Network is one of the most common data mining techniques to classify medical data, as it is a good solution for the uncertainty challenge. This paper provides a simulation of Rough Neural Network in classifying cardiotocography dataset. The paper measures the accuracy rate and consumed time during the classification process. WEKA tool is used to analyse cardiotocography data with different algorithms (neural network, decision table, bagging, the nearest neighbour, decision stump and least square support vector machine algorithm). The comparison shows that the accuracy rates and time consumption of the proposed model are feasible and efficient.

This paper is devoted for presenting new neutrosophic similarity measures between neutrosophic graphs. We propose two ways to determine the neutrosophic distance between neutrosophic vertex graphs. The two neutrosophic distances are based on the Haussdorff distance, and a robust modified variant of the Haussdorff distance, moreover we show that they both satisfy the metric distance measure axioms. Furthermore, a similarity measure between neutrosophic edge graphs, that is based on a probabilistic variant of Haussdorff distance, is introduced. The aim is to use those measures for the purpose of matching neutrosophic graphs whose structure can be described in the neutrosophic domain.

This paper aims to introduce and study some new neutrosophic fuzzy pairwise notions via neutrosophic fuzzy ideals.We, also generalize the notion of FPL-open sets. In addition to generalizing the concept of FPL-closed sets and NPL-open function, the relationship between the above new neutrosophic Fuzzy pairwise notions and there other relevantclasses are investigated. In Geographical information systems (GIS) there is a need to statistically model spatialregions with indeterminate boundary and under indeterminacy. Possible applications to GIS rules are touched upon.

In this paper, we propose a two-phase Content-Based Retrieval System for images embedded in the Neutrosophic domain. In this first phase, we extract a set of features to represent the content of each image in the training database. In the second phase, a similarity measurement is used to determine the distance between the image under consideration (query image), and each image in the training database, using their feature vectors constructed in the first phase. Hence, the N most similar images are retrieved.

In this paper, we aim to develop a new type of neutrosophic crisp set called the retract neutrosophic crisp set and shows a grayscale image in a 2D Cartesian domain with neutrosophic crisp components in the neutrosophic domain. The introduced set is a retraction of any triple structured crisp set. Whereas, the retract set deduced from any neutrosophic crisp set is coincide its corresponding star neutrosophic crisp set defined in by Salama et al. [1]. Hence we construct a new type of neutrosophic crisp topological spaces, called the retract neutrosophic crisp topological space as a retraction of the star neutrosophic topological space. Moreover, we investigate some of its properties.

Recently designing an effective intrusion detection systems (IDS) within Mobile Ad Hoc Networks Security (MANETs) becomes a requirement because of the amount of indeterminacy and doubt exist in that environment. Neutrosophic system is a discipline that makes a mathematical formulation for the indeterminacy found in such complex situations. Neutrosophic rules compute with symbols instead of numeric values making a good base for symbolic reasoning. These symbols should be carefully designed as they form the propositions base for the neutrosophic rules (NR) in the IDS. Each attack is determined by membership, nonmembership, and indeterminacy degrees in neutrosophic system. This research proposes a MANETs attack inference by a hybrid framework of Self-Organized Features Maps (SOFM) and the genetic algorithms (GA). The hybrid utilizes the unsupervised learning capabilities of the SOFM to define the MANETs neutrosophic conditional variables. The neutrosophic variables along with the training data set are fed into the genetic algorithm to find the most fit neutrosophic rule set from a number of initial subattacks according to the fitness function. This method is designed to detect unknown attacks in MANETs. The simulation and experimental results are conducted on the KDD-99 network attacks data available in the UCI machine-learning repository for further processing in knowledge discovery. The experiments cleared the feasibility of the proposed hybrid by an average accuracy of 99.3608 % which is more accurate than other IDS found in literature.

This paper presents an approach for underdetermined blind source separation that can be applied even if the number of sources is unknown. Moreover, the proposed approach is applicable in the case of separating I+3 sources from I mixtures without additive noise. This situation is more challenging and suitable to practical real world problems. Also, the sparsity conditions are not imposed unlike to those employed by some conventional approaches. Firstly, the number of source signals are estimated followed by the estimation of the mixing matrix based on the use of short time Fourier transform and rough-fuzzy clustering. Then, source signals are normalized and recovered using modified Lin's projected gradient algorithm with modified Armijo rule. The simulation results show that the proposed approach can separate I+3 source signals from I mixed signals, and it has superior evaluation performance compared to conventional approaches.

This paper is devoted for presenting new neutrosophic similarity measures between neutrosophic graphs. We propose two ways to determine the neutrosophic distance between neutrosophic vertex graphs. The two neutrosophic distances are based on the Haussdorff distance, and a robust modified variant of the Haussdorff distance, moreover we show that they both satisfy the metric distance measure axioms. Furthermore, a similarity measure between neutrosophic edge graphs, that is based on a probabilistic variant of Haussdorff distance, is introduced. The aim is to use those measures for the purpose of matching neutrosophic graphs whose structure can be described in the neutrosophic domain.

In this paper we introduce the notion of intuitionistic fuzzy ideals which is considered as a generalization of fuzzy ideals studies in [1, 2, 3, 11], the important intuitionistic fuzzy ideal has been given. The concept of intuitionistic fuzzy local function is also introduced here by utilizing theεneighborhood structure for an intuitionistic fuzzy topological space. These concepts are discussed with a view to find new intuitionistic fuzzy topology from the original one in [10, 12]. The basic structure, especially a basis for such generated intuitionistic fuzzy topologies and several relations between different intuitionistic fuzzy ideals and intuitionistic fuzzy topologies are also studied here. Finally, several properties of all investigated new notions are discussed.

Fuzzy ideals, fuzzy local function and the notion of compatibility of fuzzy ideals with fuzzy topologies were introduced and studied by D. Sarker (Fuzzy Sets and Systems 87 (1997) 117). The purpose of this paper deals with new sort of fuzzy local function namely fuzzy α-local function. Many of its characterizations, properties and connections between it and other corresponding fuzzy notions are studied. Also we introduce and study two types of fuzzy compatibility namely fuzzy α-compatibility and fuzzy weak α-compatibility of fuzzy topologies via fuzzy ideals. Possible application to superstrings and space-time are touched upon.

Neutrosophy has been introduced by Smarandache [3, 4] as a new branch of philosophy. Salama et al.[5] in 2012 introduced and studied the operations on neutrosophic sets. The purpose of this paper is to introduce a new type of data called the neutrosophic data. After given the fundamental definitions of neutrosophic set operations due to Salama [5], we obtain several properties, and discussed the relationship between neutrosophic sets and others. Finally, we discuss and derived a formula for correlation coefficient, defined on the domain of neutrosophic sets.

The aim of this paper is to introduce and study some new fuzzy pairwise notion in fuzzy bitopological ideals spaces. We also generalize the notion of fuzzy L-open sets due to Abd El-Monsef et al [1]. In addition to generalize the concept of fuzzy L-closed sets, fuzzy L-continuity and L-open functions due to Abd El-Monsef et al [1]. Relationships between the above new fuzzy pairwise notions and there other relevant classes are investigated. Recently, we define and study two different types of fuzzy pairwise functions.

The neutrosophic sets were known since 1999, and because of their wide applications and their great flexibility to solve the problems, we used these the concepts to define a new types of neutrosophic crisp closed sets and limit points in neutrosophic crisp topological space, namly [neutrosophic crisp Gem sets and neutrosophic crisp Turig points] respactvely, we stady their properties in details and join it with topological concepts. Finally we used [neutrosophic crisp Gem sets and neutrosophic crisp Turig points] to introduce of topological concepts as: neutrosophic crisp closed (open) sets, neutrosophic crisp closure, neutrosophic crisp interior, neutrosophic crisp extrior and neutrosophic crisp boundary which are fundamental for further reserch on neutrosophic crisp topology and will setrengthen the foundations of theory of neutrosophic topological spaces.

This paper is specialized with fuzzy random multi-objective unbalanced transportation data problems by using of fuzzy programming technique when the sources and destination parameters are fuzzy random variables in inequality type of constraints. In this paper, we focus on the solution procedure of the specified transportation data problems where the objective functions are minimization type and supplies and demands are replaced by the fuzzy random variables. We first convert the fuzzy random multi-objective unbalanced transportation problem into deterministic problem by using fuzzy random chance constraints approach. By introducing the concept of linear membership function of fuzzy programming, multi-objective deterministic transportation problem is converted into single objective deterministic problem and then we solve it and we obtain the optimal compromise solution. Lastly a numerical example is provided for illustration the methodology.