Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification.

Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy.

Factors affecting the adoption of metaverse in healthcare: The moderating role of digital division, and meta-culture.

This research aims to find out the factors affecting the adoption of Metaverse in healthcare. This study explores the effect of perceived ease of use, perceived usefulness, and trust on adopting Metaverse in healthcare by keeping digital division and metaculture as moderating variables. The philosophical foundation is rooted in the positivism paradigm, the methodology is quantitative, and the approach used is deductive. Data was collected in Pakistan and China through judgmental sampling from 384 respondents. Partial Least Square Structural Equation Modelling (PLS-SEM) was used to analyze the collected data. The findings validate the relationship between perceived ease of use and the adoption of metaverse with ß-value 0.236, t-value 5.207 and p-value 0.000, the relationship between perceived usefulness and the adoption of metaverse with ß-value 0.233, t-value 4.017 and p-value 0.000, and the relationship between trust and adoption of a metaverse with ß-value 0.192, t-value 3.589 and p-value 0.000. Results also show that the digital divide moderates the relation between perceived ease of use and adopting the metaverse having ß-value 0.078, t-value 1.848 and p-value 0.032. Similarly, the findings also show that the digital divide does not moderate the relationships of perceived usefulness and trust with adopting the metaverse. Moreover, the meta culture also does not moderate the relationships of perceived ease of use, usefulness, and trust with adopting the metaverse. The study contributes to theoretical research on adopting a metaverse in healthcare by examining various factors necessary for its development. It also provides guidelines for the developers and adopters of suitable metaverse technology.

Investigating the role of Cybersecurity's perceived threats in the adoption of health information systems.

Information technology is one of the most rapidly growing technologies globally. Over the last decade, its usage in healthcare has been remarkable. Over the last decade, its usage in healthcare has been remarkable. The study examines the impact of various factors as barriers to adopting the information system in healthcare. These factors are categorized into three major types: external attacks, which include phishing attacks and ransomware; employee factors, including lack of skills and the issue of information misuse; and technological factors, including complexity and vulnerability. The findings show that external attacks and technological factors are the main barriers to adopting information systems, while employee factors have no significant impact on the adoption of information systems in the healthcare industry of Pakistan. The study provides implications for healthcare policy makers, professionals and organziations regarding the successful adoption of health information system.

Investigating the role of green behavior and perceived benefits in shaping green car buying behavior with environmental awareness as a moderator.

As the planet faces the challenge of global warming, every individual and organization must adopt green practices to protect nature. The automobile industry is one of the primary industries which can contribute significantly towards sustainability. This study aims to examine the impact of green behavior and green perceived benefits on the green buying behaviors of automobiles. The research also explores the moderating influence of environmental awareness on the mechanism. The research is based on a quantitative method for which primary data was gathered from 406 respondents across Pakistan, China and Saudi Arabia via Quota-based purposive sampling. The gathered data was analyzed via SmartPLS. The results show that green behavior and perceived benefits positively and significantly influence green buying behavior. The findings also show the moderating role of environmental awareness on green behavior towards green buying and show no impact on the perceived benefits towards buying behavior. The study has practical and theoretical implications for managers, researchers, policymakers and institutions in the context of green automobile development and businesses. The study also contributes to the attainment of sustainable development goals.

Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network.

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method's performance.

SCBC: Smart city monitoring with blockchain using Internet of Things for and neuro fuzzy procedures.

The security of the Internet of Things (IoT) is crucial in various application platforms, such as the smart city monitoring system, which encompasses comprehensive monitoring of various conditions. Therefore, this study conducts an analysis on the utilization of blockchain technology for the purpose of monitoring Internet of Things (IoT) systems. The analysis is carried out by employing parametric objective functions. In the context of the Internet of Things (IoT), it is imperative to establish well-defined intervals for job execution, ensuring that the completion status of each action is promptly monitored and assessed. The major significance of proposed method is to integrate a blockchain technique with neuro-fuzzy algorithm thereby improving the security of data processing units in all smart city applications. As the entire process is carried out with IoT the security of data in both processing and storage units are not secured therefore confidence level of monitoring units are maximized at each state. Due to the integration process the proposed system model is implemented with minimum energy conservation where 93% of tasks are completed with improved security for about 90%.

An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce.

Artificial Intelligence (AI) has become essential to Electronic-Commerce technology over the past decades. Its fast growth has changed the way consumers do online shopping. Using the Technology Acceptance Model (TAM) as a theoretical framework, this research examines how AI can be made more effective and profitable in e-commerce and how entrepreneurs can make AI technology to assist in achieving their business goals. In this regard, an online survey was conducted from the online purchasers of e-commerce firms. The Partial Least Square (PLS) Smart was used to examine the data. The broadly used TAM was identified as an appropriate hypothetical model for studying the acceptance of AI technology in e-commerce. The findings of this study show that Subjective Norms positively impact Perceived Usefulness (PU) and Pursued Ease of Use (PEU), trust has a positive effect on PEU, and PEU positively impacts PU and attitudes toward use. Similarly, PU also has a positive effect on attitudes toward use and intention to use. Furthermore, the findings do not support the impact of Trust on PU and attitudes towards behavioural intention to use. Lastly, behavioural intention to use positively impacted the actual use of AI technology. This study adds theoretical and practical knowledge for adopting the TAM model in the E-commerce sector. It helps entrepreneurs to implement the TAM model in their business to use AI in a better and more appropriate way.

Blockchain technology in the pharmaceutical industry: a systematic review.

Blockchain technology is accelerating digital transformation across multiple industries, including the pharmaceutical industry. The pharmaceutical industry suffers from a lack of transparency, difficulty tracking products, lack of trust, and the shipment of expired products. Blockchain technology has been applied to solve several of these problems. In this paper, we present a systematic review of the literature focusing on the adoption of blockchain technology in the pharmaceutical industry. We collected, analyzed, qualified, and discussed studies retrieved from seven databases. The initial search yielded 2,185 papers, which were screened, discussed, voted on, critically appraised, and collected by a snowball workflow that finally yielded 38 papers. The blockchain application areas covered in the papers were classified as counterfeit drug prevention, drug distribution, tracking and tracing, and safety and security. The most frequent category was counterfeit drug prevention, which is consistent with the primary objective of the pharmaceutical industry. The newer topics discussed in this study were data governance, data quality, pharmaceutical turnover, and prescription drug monitoring. We discuss issues surrounding each of these topics and research studies, along with their limitations and solutions. We also examine the challenges and future research directions of applying blockchain technology in the pharmaceutical industry.

Data privacy during pandemics: a systematic literature review of COVID-19 smartphone applications.

BACKGROUND: On January 8, 2020, the Centers for Disease Control and Prevention officially announced a new virus in Wuhan, China. The first novel coronavirus (COVID-19) case was discovered on December 1, 2019, implying that the disease was spreading quietly and quickly in the community before reaching the rest of the world. To deal with the virus' wide spread, countries have deployed contact tracing mobile applications to control viral transmission. Such applications collect users' information and inform them if they were in contact with an individual diagnosed with COVID-19. However, these applications might have affected human rights by breaching users' privacy. METHODOLOGY: This systematic literature review followed a comprehensive methodology to highlight current research discussing such privacy issues. First, it used a search strategy to obtain 808 relevant papers published in 2020 from well-established digital libraries. Second, inclusion/exclusion criteria and the snowballing technique were applied to produce more comprehensive results. Finally, by the application of a quality assessment procedure, 40 studies were chosen. RESULTS: This review highlights privacy issues, discusses centralized and decentralized models and the different technologies affecting users' privacy, and identifies solutions to improve data privacy from three perspectives: public, law, and health considerations. CONCLUSIONS: Governments need to address the privacy issues related to contact tracing apps. This can be done through enforcing special policies to guarantee users privacy. Additionally, it is important to be transparent and let users know what data is being collected and how it is being used.

A systematic literature review on the usability of mobile applications for visually impaired users.

Interacting with mobile applications can often be challenging for people with visual impairments due to the poor usability of some mobile applications. The goal of this paper is to provide an overview of the developments on usability of mobile applications for people with visual impairments based on recent advances in research and application development. This overview is important to guide decision-making for researchers and provide a synthesis of available evidence and indicate in which direction it is worthwhile to prompt further research. We performed a systematic literature review on the usability of mobile applications for people with visual impairments. A deep analysis following the Preferred Reporting Items for SLRs and Meta-Analyses (PRISMA) guidelines was performed to produce a set of relevant papers in the field. We first identified 932 papers published within the last six years. After screening the papers and employing a snowballing technique, we identified 60 studies that were then classified into seven themes: accessibility, daily activities, assistive devices, navigation, screen division layout, and audio guidance. The studies were then analyzed to answer the proposed research questions in order to illustrate the different trends, themes, and evaluation results of various mobile applications developed in the last six years. Using this overview as a foundation, future directions for research in the field of usability for the visually impaired (UVI) are highlighted.