A Comparative Study of Consumer Perception of Product Quality: Chinese versus Non-Chinese Products

Product quality is a critical determinant of consumer satisfaction. The demand for a product depends upon the quality that a manufacturer is providing to their consumers. China, which is a growing economic power, exports its manufactured goods to the entire global markets. Chinese goods have been successful to capture market because of its competitive price strategy as compared to the products of other countries. The major problem with the Chinese products is that these are perceived as of relatively inferior quality in comparison to the products of other countries. This study is an attempt to assess the perceptions of customers regarding price and quality aspects of Chinese and non Chinese products. To compare the relative effectiveness of price and quality, the concepts of perceived life and perceived value are used. It is found that the Chinese products are perceived as price effective but the area of product quality requires immediate attention because Chinese products are perceived as of low qualit

An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements

Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images

An examination of two methods of measuring inconsistency

Previous research has provided evidence for the notion that there are varying levels of inconsistency between individuals when responding to questionnaires with multiple response items. Specifically, there are individual differences in how consistently persons respond to items from the same dimension in a questionnaire (Reddock, Biderman & Nguyen, 2011). Currently, there is not a consensus on how inconsistency should be measured. In the present study inconsistency of responses to the IPIP Big Five questionnaire was measured. Two response formats permitting measurement of inconsistency were compared - a frequency-based format (FB) vs. a traditional Likert scale format. Furthermore, in an effort to study inconsistency in a broader context, the relationships of social desirability and ADHD to inconsistency were examined. The results provided no evidence for convergent validity between the two measures, discriminant validity for each measure, no evidence of a relationship between BIDR and inconsistency, but a positive relationship between FB based inconsistency and scores on the ADHD measure. Implications and limitations of the study are discussed

Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond

In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts. This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies, with a particular focus on the healthcare domain. Delving deeply, it explores a multitude of facets including transparency, adept data management, human oversight, educational imperatives, and international collaboration within the realm of AI advancement. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The second contribution of the article is the in-depth and thorough discussion of the limitations inherent to AI systems. It astutely identifies potential biases and the intricate challenges of navigating multifaceted contexts. Lastly, the article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks. Simultaneously, it aptly illustrates the adaptability of the ethical framework proposed herein, positioned skillfully to surmount emergent challenges

Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance

People all over the globe are affected by pneumonia but deaths due to it are highest in Sub-Saharan Asia and South Asia. In recent years, the overall incidence and mortality rate of pneumonia regardless of the utilization of effective vaccines and compelling antibiotics has escalated. Thus, pneumonia remains a disease that needs spry prevention and treatment. The widespread prevalence of pneumonia has caused the research community to come up with a framework that helps detect, diagnose and analyze diseases accurately and promptly. One of the major hurdles faced by the Artificial Intelligence (AI) research community is the lack of publicly available datasets for chest diseases, including pneumonia . Secondly, few of the available datasets are highly imbalanced (normal examples are over sampled, while samples with ailment are in severe minority) making the problem even more challenging. In this article we present a novel framework for the detection of pneumonia. The novelty of the proposed methodology lies in the tackling of class imbalance problem. The Generative Adversarial Network (GAN), specifically a combination of Deep Convolutional Generative Adversarial Network (DCGAN) and Wasserstein GAN gradient penalty (WGAN-GP) was applied on the minority class ``Pneumonia'' for augmentation, whereas Random Under-Sampling (RUS) was done on the majority class ``No Findings'' to deal with the imbalance problem. The ChestX-Ray8 dataset, one of the biggest datasets, is used to validate the performance of the proposed framework. The learning phase is completed using transfer learning on state-of-the-art deep learning models i.e. ResNet-50, Xception, and VGG-16. Results obtained exceed state-of-the-art

Preparation, Characterization, and Catalytic Evaluation of Metal Containing MCM-41-Based Hydrodesulfurization Catalysts

In this study, the preparation of nickel and cobalt incorporated MCM-41 (metal-MCM-41) with Si/Al = 10, Si/Mo = 10 and Si/Co and Si/Ni as 50 was done. Then molybdenum was impregnated on Ni-MCM-41 and Co-MCM-41 by incipient wetness impregnation method. MCM-41 based catalysts were characterized by XRD, surface area, pore volume, pore-size distribution, and temperature programmed reduction (TPR). The catalysts were evaluated for hydrodesulfurization of model compounds thiophene and benzothiophene in pulse reactor at atmospheric pressure and at different temperatures. HDS of dibenzothiophene dissolved in dodecane, was also performed in a batch autoclave reactor at high pressure and temperature. The results of hydrodesulfurization activity indicate that when impregnated with molybdenum the prepared metal-MCM-41 showed higher activity as compared to commercial catalyst. Mo-CoMCM-41 showed highest conversion per mole of metal in hydrodesulfurization of thiophene and benzothiophene. For comparison purposes, a sample of NiMo-Y zeolite catalyst was also prepared and evaluated

Framework for reliable, real-time facial expression recognition for low resolution images

International audienceAutomatic recognition of facial expressions is a challenging problem specially for low spatial resolution facial images. It has many potential applications in human-computer interactions, social robots, deceit detection, interactive video and behavior monitoring. In this study we present a novel framework that can recognize facial expressions very efficiently and with high accuracy even for very low resolution facial images. The proposed framework is memory and time efficient as it extracts texture features in a pyramidal fashion only from the perceptual salient regions of the face. We tested the framework on different databases, which includes Cohn-Kanade (CK+) posed facial expression database, spontaneous expressions of MMI facial expression database and FG-NET facial expressions and emotions database (FEED) and obtained very good results. Moreover, our proposed framework exceeds state-of-the-art methods for expression recognition on low resolution images