Analysis of neutrosophic multiple regression

The idea of Neutrosophic statistics is utilized for the analysis of the uncertainty observation data. Neutrosophic multiple regression is one of a vital roles in the analysis of the impact between the dependent and independent variables. The Neutrosophic regression equation is useful to predict the future value of the dependent variable. This paper to predict the students' performance in campus interviews is based on aptitude and personality tests, which measures conscientiousness, and predict the future trend. Neutrosophic multiple regression is to authenticate the claim and examine the null hypothesis using the F-test. This study exhibits that Neutrosophic multiple regression is the most efficient model for uncertainty rather than the classical regression model

Interval type‑2 fuzzy aggregation operator in decision making and its application

Type-2 fuzzy sets (T2FSs) can deal with higher modeling and uncertainties which exist in the real-world application, specifically in the control systems. Particularly the climate changes are always uncertain and thus, the type-2 fuzzy controller is an effective system to handle those situations. Polyhouse is a methodology used to cultivate the plants. It breaks the seasonal hurdle of the formulation and it is also suitable for the conflictive climate conditions. Controlling and directing internal parameters of the polyhouse play an essential role in the growth of the plant. Among those, humidity is an important element when one deals with the growth of the plant in polyhouse. It affects the weather, as well as the global change of the climate and hence, the inner climate of the polyhouse will be disturbed. In this paper, operational laws for triangular interval type-2 fuzzy numbers and derived triangular interval type-2 weighted geometric (TIT2WG) operator with their desired mathematical properties using Dombi triangular norms. Also, humidity control is analyzed using interval type-2 fuzzy controller (IT2FC) with the use of derived aggregation operator which is the aim of the paper. Further stability of the system has been analyzed by applying four different defuzzification methods and the method is recommended which gives a better response

Quantum Bootstrap Aggregation

We set out a strategy for quantizing attribute bootstrap aggregation to enable variance-resilient quantum machine learning. To do so, we utilise the linear decomposability of decision boundary parameters in the Rebentrost et al. Support Vector Machine to guarantee that stochastic measurement of the output quantum state will give rise to an ensemble decision without destroying the superposition over projective feature subsets induced within the chosen SVM implementation. We achieve a linear performance advantage, O(d), in addition to the existing O(log(n)) advantages of quantization as applied to Support Vector Machines. The approach extends to any form of quantum learning giving rise to linear decision boundaries

New-normal market entry mode for pharmaceuticals: an Internet of Things (IoT) market entry framework stemming from COVID-19

Purpose: To determine new-normal uncertainty considerations stemming from the covid-19 pandemic to consider within transaction-cost analysis for pharmaceuticals. To propose new-normal market entry strategies to address the uncertainty as a result of covid-19’s implications and provide for lack of knowledge and information in an uncertain business environment by way of Internet of Things (IoT) ecosystem for pharmaceutical market entry. Methodology: In this paper, we focus on the uncertainty facet within transaction-cost analysis consideration and utilise a descriptive three-case study approach taking in Johnson and Johnson (J&J), GlaxoSmithKline (GSK) and Novartis to present an ADO (Antecedent-Decisions-Outcomes) understanding of their usual market entry approach, the approach undertaken during the pandemic and the outcomes thereafter facilitating new-normal uncertainty considerations to factor in. Further with this insight, we develop a conceptual framework addressing the transaction-cost analysis implications of uncertainties toward lack of knowledge and information for new-normal market entry approach and operating strategy for pharmaceuticals applicable due to IoT (Internet of Things). Findings: Uncertainty (external and internal) is different now in the new-normal business environment for pharmaceuticals and boils down to acute shortage of knowledge and information impact to make an appropriately informed decision. Therefore, considering the changed factors to consider, pharmaceuticals need to be able to undertake market entry with vaccines and medicines by way of IoT thereby enabling, the filling of the gap via real-time data access and sharing including enhancing predictive analysis for sustenance. Originality: It is the first study to our knowledge that throws light on transaction-cost analysis theory’s uncertainty facet for pharmaceuticals. It is also the first study that provides new-normal market entry strategy for pharmaceutical companies built on interoperability of real-time IoT

Comparative analysis of filtering methods in fuzzy C-means: environment for DICOM image segmentation

Medical image analysis was done using a sequential application of low-level pixel processing and mathematical modeling to develop rule-based systems. During the same period, artificial intelligence was developed in analogy systems. In the 1980s magnetic resonance or computed tomography imaging system has been introduced that encode and decode the output of the images. Digital imaging and communications in medicine (DICOM) has improved the communication mechanism in the medical environment. In products such as CT, MR, X-ray, NM, RT, US, etc., DICOM is used for image storing, printing the information about the patient’s condition, and transmitting the correct information about the radiological images. It involves a file format and protocol in communication networks. It is useful for receiving images and patient data in DICOM format. DICOM format has been widely adopted to all medical environments and derivations from the DICOM standard are used into other application areas. DICOM is the basis of digital imaging and communication in nondestructive testing and in security. DICOM data consist of many attributes including information such as name, ID, and image pixel data. A single DICOM object can have only one attribute containing pixel data. Pixel data can be compressed using a variety of standards, including JPEG, JPEG Lossless, JPEG 2000, and Run-length encoding

Quantum error-correcting output codes

Quantum machine learning is the aspect of quantum computing concerned with the design of algorithms capable of generalized learning from labeled training data by effectively exploiting quantum effects. Error-correcting output codes (ECOC) are a standard setting in machine learning for efficiently rendering the collective outputs of a binary classifier, such as the support vector machine, as a multi-class decision procedure. Appropriate choice of error-correcting codes further enables incorrect individual classification decisions to be effectively corrected in the composite output. In this paper, we propose an appropriate quantization of the ECOC process, based on the quantum support vector machine. We will show that, in addition to the usual benefits of quantizing machine learning, this technique leads to an exponential reduction in the number of logic gates required for effective correction of classification error

Nuclear magnetic resonance spectroscopy: Abnormal splitting of ethyl groups due to molecular asymmetry

Nuclear magnetic resonance (n.m.r.) spectroscopy provides an excellent means for qualitative identification of ethyl groups by use of the familiar three-four pattern of spin-spin splitting (1). It has been observed previously (2) that the methylene protons of systems of the type R-CH2-CR1R2R3 (where R1 can be the same as R or different) may be magnetically nonequivalent and display AB rather than A2-type spectra (3). We now wish to report several examples of this type of behavior with ethyl groups, particularly ethoxy groups, knowledge of which could be important to anyone using n.m.r. for organic qualitative analysis

Miniaturization of photonic waveguides by the use of left-handed materials

We propose the use of a left-handed material in an optical waveguide structure to reduce its thickness well below the wavelength of light. We demonstrate that a layer of left-handed material, added to the cladding of a planar waveguide rather than to its core, allows for good light confinement in a subwavelength thin waveguide. We attribute the observed behavior to the change in phase evolution of electromagnetic waves in the guide. This technique can be used for the miniaturization of photonic integrated circuits.Comment: 4 pages, 4 figure

Stressful eating indulgence by Generation Z: a cognitive conceptual framework of new age consumers’ obesity

Purpose: To delineate the phenomenon of stressful eating within generation Z due to the times they are living in. To extract propositions which elucidate phases of stressful eating within Zers. Methodology: Based on relevant literature on consumer obesity, theories of pure impulse buying and reasoned action, cognitive constructs eminent for reasoned conditioned behaviour is extracted. Followed by extraction of the reasoned conditioned behaviour and its cognitive constructs within Zers. Thereafter a conceptual framework is developed with propositions of stressful eating within Zers. Findings: Zers indulge in reasoned conditioned behaviour initially owing to their healthy understanding insights, and the activations of cognitive capacities within them due to the law of effect. The law of effect is cyclical after the first reasoned consumption among Zers, leading to obesity and constricting self-controlling behaviour. Originality Value: It is the first study that provides a deep understanding of the cognitive mechanism orienting generation Z’s stressful eating indulgence even though they have higher healthy lifestyle understandings