Lung cancer has been the leading cause of cancer death for many decades. With the advent of artificial intelligence, various machine learning models have been proposed for lung cancer detection (LCD). Typically, challenges in building an accurate LCD model are the small‐scale datasets, the poor generalizability to detect unseen data, and the selection of useful source domains and prioritization of multiple source domains for transfer learning. In this paper, a multiround transfer learning and modified generative adversarial network (MTL‐MGAN) algorithm is proposed for LCD. The MTL transfers the knowledge between the prioritized source domains and target domain to get rid of exhaust search of datasets prioritization among multiple datasets, maximizing the transferability with a multiround transfer learning process, and avoiding negative transfer via customization of loss functions in the aspects of domain, instance, and feature. In regard to the MGAN, it not only generates additional training data but also creates intermediate domains to bridge the gap between the source domains and target domains. 10 benchmark datasets are chosen for the performance evaluation and analysis of the MTL‐MGAN. The proposed algorithm has significantly improved the accuracy compared with related works. To examine the contributions of the individual components of the MTL‐MGAN, ablation studies are conducted to confirm the effectiveness of the prioritization algorithm, the MTL, the negative transfer avoidance via loss functions, and the MGAN. The research implications are to confirm the feasibility of multiround transfer learning to enhance the optimal solution of the target model and to provide a generic approach to bridge the gap between the source domain and target domain using MGAN.

The danger of distributed denial of service (DDoS) attacks has grown in tandem with the proliferation of intelligent information systems. Because of the sheer volume of connected devices, constantly shifting network circumstances, and the need for instantaneous reaction, conventional DDoS detection methods are inadequate for the IoT. In this context, this study aims to survey the current state of the art in the topic by reading relevant articles found in the Scopus database, with a brief overview of the IoT and DDoS as this study examines neural networks and their applicability to DDoS detection. Finally, a decision tree-based model is developed for the detection of DDoS attacks. The analysis sheds light on the present trends and issues in this field and suggests avenues for further study.

Integrating virtual reality (VR) and augmented reality (AR) technology into online stores enables more immersive and engaging shopping experiences, which is crucial for businesses to succeed in today's competitive e-commerce market. These technologies offer unique, personalized experiences that consider the preferences and requirements of each customer. This research aims to understand better the most recent developments in AR and VR technology, and how these technologies might be used in e-commerce. Multiple databases were used to conduct a thorough search, and the inclusion criteria focused on using AR and VR in e-commerce. A total of 55 papers were found and categorized based on the research methodologies and issues used. Based on the findings of the research paper, it can be concluded that integrating AR and VR technologies in e-commerce has significant potential to improve various aspects of the online shopping experience.

This paper delves into the dynamic intersection of athletic psychology and emerging technologies, aiming to understand their interplay and implications for sports performance. The study examines the latest research and literature in this field, encompassing the use of social media, digital devices, and virtual reality as technological advancements. It explores the impact of these technologies on athlete psychology, mental resilience, motivation, and goal setting. By analyzing country-specific scientific production, author contributions, and keyword trends, the paper provides insights into the global landscape of research in athletic psychology and emerging technologies. The findings contribute to a better understanding of the evolving relationship between technology and athlete psychology, offering potential avenues for optimizing performance, mental well-being, and training strategies in the realm of sports.

The implementation of industrial robots across various sectors has ushered in unparalleled advancements in efficiency, productivity, and safety. This paper explores the domain of semantic trajectory planning in the area of industrial robotics. By adeptly merging physical constraints and semantic knowledge of environments, the proposed methodology enables robots to navigate complex surroundings with utmost precision and efficiency. In a landscape marked by dynamic challenges, the research positions semantic trajectory planning as a linchpin in fostering adaptability. It ensures robots interact safely with their surroundings, providing vital object detection and recognition capabilities. The proposed ResNet model exhibits remarkable classification performance, bolstering overall productivity. The study underscores the significance of this approach in addressing real-world industrial applications while emphasizing accuracy, precision, and enhanced productivity.

The facility allocation of the supply chain is critical since it directly influences cost efficiency, customer service, supply chain responsiveness, risk reduction, network optimization, and overall competitiveness. When enterprises deploy their facilities wisely, they may achieve operational excellence, exceed customer expectations, and obtain a competitive advantage in today's volatile business climate. Due to this reason, a multi-objective facility allocation problem is introduced in this research with cooperative-based multi-level backup coverage considering distance-based facility attractiveness. The facility of the coverage is further described as two different layers of the coverage process, where demand can be covered as full, partial, and no coverage by their respective facilities. The main objectives of this facility allocation problem are to maximize the coverage of the facility to maximize overall facility coverage in the supply chain network and simultaneously minimize the overall cost.

Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.

Applying sharding protocol to address scalability challenges in alliance chain is popular. However, inevitable cross-shard transactions significantly hamper performance even at low ratios, negating scalability benefits when they dominate as shard scale grows. This article proposes a new sharding protocol suitable for alliance chain that reduces cross-shard transaction impact, improving system performance. It adopts a directed acyclic graph ledger, enabling parallel transaction processing, and employs dynamic transaction confirmation consensus for simplicity. The protocol's sharding process and node score mechanism can deter malicious behavior. Experiments show that compared with mainstream sharding protocols, the protocol performs better when affected by cross-shard transactions. Moreover, its throughput has shown improvement compared to high-performance protocols without cross-shard transactions. This solution suits systems requiring high throughput and reliability, maintaining a stable performance advantage even as cross-shard transactions increase to the usual maximum ratio.

This paper proposes a hierarchical framework for industrial Internet device authentication and trusted access as well as a mechanism for industrial security state perception, and designs a cross-domain authentication scheme for devices on this basis. The scheme obtains hardware device platform configuration register (PCR) values and platform integrity measure through periodic perception, completes device identity identification and integrity measure verification when device accessing and data transmission requesting, ensures secure and trustworthy access and interoperation of devices, and designs a cross-domain authentication model for trustworthy access of devices and related security protocols. Through the security analysis, this scheme has good anti-attack abilities, and it can effectively protect against common replay attacks, impersonation attacks, and man-in-the-middle attacks.

Virtual worlds are progressing toward a holistic abstraction of the metaverse. While there is abundant literature and synthesis on virtual worlds and related constructs, the linkages between above scholarly work and the “metaverse” are scarce. This research study addresses this gap by focusing on three specific research pursuits: a comprehensive definition of the metaverse that subsumes virtual world literature and looks at the metaverse as a sociotechnical stack, exploring the design elements of the metaverse, and a synthesis of future research direction associated with metaverse. For achieving the above goals, a hybrid research methodology comprising bibliometric analysis and a rigorous qualitative analysis of case studies across four major metaverse players with varied end goals was employed. The interpretive qualitative analysis was further distilled by mapping the emergent themes to the theoretical lens of affordances. This work presents a novel framework of metaverse design, establishing theoretical linkages between the sociotechnical fabric and applications of the metaverse.

The emergence of the metaverse, a virtual reality space, has ushered in a new era of digital experiences and interactions in global information systems. With its unique social norms and behaviors, this new world presents exciting opportunities for users to connect, socialize, and explore. However, as people spend more time in the metaverse, it has become increasingly apparent that the issue of cyberbullying needs to be addressed. Cyberbullying is a serious problem that can harm victims psychologically and physically. It involves using technology to harass, intimidate, or humiliate individuals or groups in global information systems. The risk of cyberbullying is high in the metaverse, where users are often anonymous. Therefore, it is crucial to establish a safer and more respectful culture within the metaverse to detect and prevent such incidents from happening.