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The reliable transcranial imaging of brain inner structures for diagnostic purposes is deemed crucial owing to the decisive importance and contribution of the brain in human life. The purpose of this paper is to investigate the potential application of medical ultrasounds to transcranial imaging using advanced techniques, such as the total focussing method.

Initially, the fundamental details of the total focussing method are presented, while the skull properties, such as the increased acoustic velocity and scattering, are thoroughly examined. Although, these skull characteristics constitute the main drawback of typical transcranial ultrasonic propagation algorithms, they are exploited to focus the acoustic waves towards the brain. To this goal, a virtual source is designed, considering the wave refraction, to efficiently correct the reconstructed brain image. Finally, the verification of the novel method is conducted through numerical simulations of various realistic setups.

The theoretically designed virtual source resembles a focussed sensor; therefore, the directivity increment, owing to the propagation through the skull, is confirmed. Moreover, numerical simulations of real-world scenarios indicate that the typical artifacts of the conventional total focussing method are fully overcome because of the increased directivity of the proposed technique, while the reconstructed image is efficiently corrected when the proposed virtual source is used.

A new systematic methodology along with the design of a flexible virtual source is developed in this paper for the reliable and precise transcranial ultrasonic image reconstruction of the brain. Despite the slight degradation owing to the skull scattering, the combined application of the total focussing method and the featured virtual source can successfully detect arbitrary anomalies in the brain that cannot be spotted by conventional techniques.

Through a review of historically famous cases and a chronicle of neurotechnology development, this chapter discusses brain structure and brain function as two distinct yet interrelated paths to understand the relative contributions of anatomical and physiological mechanisms to the human brain–behavior relationship. From an organizational neuroscience perspective, the chapter describes over a dozen neuroimaging technologies that are classified under four groupings: morphologic, invasive metabolic, noninvasive metabolic, and electromagnetic. We then discuss neuroimaging variables that may be useful in social science investigations, and we underscore electroencephalography as a particularly useful modality for the study of individuals and groups in organizational settings. The chapter concludes by considering emerging science and novel brain technologies for the organizational researcher as we look to the future.

Political science is often derided for being a “soft” science, one unable to generate hard predictions about political behavior, or without the ability to test its hypotheses, unlike physics, biology, or, among the social sciences, economics. Standards of hypothesis testing, data collection, and testing were unfairly seen to be lacking in comparison with the hard sciences. Accordingly, political scientists often had to struggle to have the knowledge produced about political behavior taken seriously. It would not be too remiss to identify an inferiority complex among political scientists, when they discussed the pantheon of scientific disciplines and their low position in it.

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases.

The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model.

From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images.

This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

Since Islamic markets are growing substantially, there is an urgent need to gain a better understanding of how Muslim consumers perceive products from a religious perspective. The purpose of this paper is to investigate the brain responses of Muslim consumers to Halal and non-Halal products using a functional magnetic resonance imaging (fMRI) technology.

The research model is a simplified version of the theory of planned behavior. The initial experiment began by asking participants to divide a set of images into two groups: Halal and non-Halal products. The fMRI experiment uses a blocked design approach to capture brain activities resulting from presenting the two groups of images to participants, and to record the strength of their attitudes toward purchasing the products.

Across all participants, the level of brain activation in the ventromedial prefrontal cortex increased significantly when Halal images were presented to them. The same results emerged when the Halal images showed raw and cooked meat. The variations in the results may be due to the high emotional sensitivity of Muslim consumers to using religious products.

This study uses a unique approach to monitor brain activity to confirm that consumers from specific market segments respond differently to market products based on their internal beliefs. Findings from this study provide evidence that marketing managers targeting Muslim markets should consider the sensitivity of presenting products in ways that reflect religious principles, in order to gain higher acceptance in this market segment.

Although the literature reports considerable research on Muslim consumers’ behavior, most of the previous studies utilize conventional data collection approaches to target broad segments of consumers by using traditional products. This paper is the first to track the reactions of the Muslim consumer segment to specific types of market products.

As one of the most dynamic and consequential areas of biomedical research, neuroscience must be analyzed in a broader political context. Research initiatives, individual use, and aggregate social consequences of unfolding knowledge about the brain and the accompanying applications require particularly close scrutiny because of the centrality of the brain itself to human behavior and thoughts. As one of the last frontiers of medicine, neuroscience has strong support because it promises to benefit many patients suffering from an array of behavioral, neurological, and mental disorders and injuries. Given the inevitability of expanded strategies for exploration and therapy of the brain, it is important that the political issues surrounding their application be clarified and debated before such techniques fall into routine use.

Halal markets are rapidly growing in terms of market size and global coverage; therefore, there is a critical demand to have a deeper understanding of the consumption behavior of Muslim consumers. This study aims to explore the influence of using the Halal logo on Muslim consumers’ attitudes toward food products using the neuroscience technology of functional magnetic resonance imaging (fMRI).

The theory of planned behavior represents the foundation of this research, where consumer attitudes during an fMRI experiment were evaluated based on two different groups of images: images of just the Halal logo and images of meat labeled with the Halal logo. The study used the blocked design approach to track brain responses produced from displaying the two groups of images to study participants, where brain activity represents participants’ attitudes toward selecting the products.

There were no significant variations in brain activity when participants viewed Halal and non-Halal logos; in contrast, there were significant brain changes in the ventromedial prefrontal cortex region when meat images were labeled with Halal and non-Halal logos. This suggests that the Halal logo only has an influence on perception when it co-occurs with a product.

Tracking Muslim consumption patterns is important for managers to be able to establish strategies to target Muslim consumers. This study uses a unique technique to study the behavioral attitude of a rapidly growing market segment, which can help marketing managers tailor their advertisement strategies to be more effective.

Previous research on the consumption of Halal products uses conventional approaches to study the influence of the Halal logo; however, to the best of the authors’ knowledge, this study is the first to explore the influence of the Halal logo on Muslim consumers’ attitudes using fMRI technology.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

The purpose of this paper is to provide a brief overview of the methodology of several brain imaging techniques and in particular, functional magnetic resonance imaging (fMRI) and its potential implications for market research. The aim is to enable the reader both to understand this emerging methodology and to conduct independent research in the area.

A short introduction on current neuroimaging methods used in behavioral neuroscience is provided by means of a literature review. The ensuing discussion focuses on fMRI as the currently most popular neuroimaging technique. Having described the fMRI methodology, an outline of the analysis of functional neuroimaging data follows, after which there is a discussion of some key research issues.

Although in its infancy, fMRI seems to be a useful and promising tool for market researchers. Initial studies in the field reveal that fMRI is able to shed light on subconscious processes such as affective aspects of consumer behavior.

Because brand positioning, advertising strategies and even pricing strategies are often based on constructs such as emotions, neuropsychological findings and methods should have important implications for practitioners in the field of brand management and advertising. Nonetheless, far more basic research is needed before fMRI can be adopted for marketing practice.

This paper is one of the first in the marketing literature to provide a methodological overview of fMRI and discuss the potential implications for marketing research.

The purpose of this paper is to explore the influence of destination images on tourists’ behavioral intention to select a destination for their next vacation. Most of previous studies investigated this relationship by interacting with tourists during their stay in the destination. However, this research examines the impact of destination images before tourists visit a destination, using functional technological-oriented magnetic resonance imaging (fMRI) approach to track brain activation during the decision to select a destination.

The proposed model is adopted from the theory of planned behavior. Study participants divide a set of hotel destination images into two groups: attractive and non-attractive destination images. A blocked design experiment was used during fMRI scan to track brain activities resulting from presenting the two groups of images to participants, and record the strength of their intention to visit the attractive destination.

The level of brain activation at the ventromedial prefrontal cortex (vmPFC) increased when participants were asked to assess the attractive destination images compared with the level of activation for non-attractive ones. Also, the positive attitude toward an attractive destination led to higher intention to visit that destination.

This study enhances the authors’ understanding of how tourists analyze destination images to reach a decision on future action. It can also be used to help destination managers define an advertisement strategy that makes their destination more attractive.

Although the literature reports considerable research on destination image and its influence on tourists intention, this is the first exploratory study to use the fMRI technology to investigate tourists’ attitude toward destination images.