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.

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.

A healthy diet and daily physical activity are a cornerstone in preventing serious diseases and conditions such as heart disease, diabetes, high blood pressure, and hypertension. They also play an important role in the healthy growth and cognitive development for young and old people. Thus, this paper presents a new restaurant advisory system (RAS) using artificial intelligence (AI) techniques such as machine learning, decision tree, and rule-based methods. The proposed system makes a smart decision based on the user's input information to generate a list of appropriate meals that fit his/her health condition. For accuracy and efficiency measurement procedure in the decision-making process, a dataset from 1100 participants suffering from several diseases such as allergy, age, and body has been created and validated. The performance of the RAS was tested using Visual Basic.net Framework and prolog language. The RAS achieves an accuracy of 100% by testing 30 different live cases.

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.

Using self-powered sensors, traffic data may be collected continuously, efficiently, and sustainably once connected autonomous vehicles (CAVs) are a part of metaverse technology. Metaverse self-powered sensors can capture uninterrupted data that allow for activities such as the management of the traffic network, the optimization of transportation facilities, and the management of urban and intercity journeys to be performed. In addition, metaverse technology creates a new field of study. Evaluating the systems involved in current transportation activities together with the metaverse can increase the efficiency and sustainability of transportation. The main purpose of this study is to prioritize four alternatives of CAVs in metaverse with self-powered sensors using a novel decision making model. The proposed hybrid decision making framework includes two stages. In the first stage the fuzzy full consistency method (fuzzy FUCOM) is applied to find the weighting coefficients of criteria. In the second stage, a fuzzy non-linear model based on fuzzy Aczel-Alsina functions (fuzzy Aczel-Alsina weighted assessment - ALWAS method) is defined to rank the alternatives. Four alternatives are defined and evaluated using twelve different criteria under four headings, namely, technical advancement, environmental, implementation, and financial aspects. A case study has been created for the experts to evaluate the alternatives most effectively. The results of the study indicate that using self-powered sensors for integrating real-time traffic management in the metaverse is the most advantageous alternative.

This paper explores the role of AI and database management in organizational transformation using insights from Twitter data. By analyzing 30,000 English-language tweets with methods such as word analysis, topic modeling, network analysis, sentiment analysis, and emotion analysis, the study reveals a strong correlation between AI and digital transformation. The findings show positive sentiment and optimism about AI's potential. This research highlights the importance of social influence, perceived trust, and awareness in AI adoption, offering valuable insights for researchers and practitioners. Despite relying on Twitter data, the study provides practical guidance for leveraging AI in digital transformation efforts.

With COVID-19-led business disruption and businesses expediting their move towards digital transformation, Industry experts observed a phenomenon of increased adoption of Open Source Software and database management systems to digitalize the business while keeping costs low. This research has studied this phenomenon using a three-step approach: using the collective intelligence of Twitter without the context of COVID-19, Twitter data with the context of COVID-19, and empirical validation with actual monthly downloads data of open source projects from SourceForge in Pre-COVID and COVID-19 time periods. The research finds that although the COVID-19 pandemic has triggered a digital transformation and the use of database management systems in many organizations, but there is no statistically significant increased use that can be attributed to a crisis response due to the pandemic.

Timely intelligent decision support systems (DSS) are increasingly important for the sustainable development of entrepreneurship. Global information management plays an important role in accurate DSS. Judgments can be made more quickly, accurately, and objectively thanks to the availability of large data and sophisticated artificial intelligence in the realm of quantitative smart decisions. In this context, this research analyzes the contribution of global information management for sustainable business development through DSS. This paper used the Scopus database to collect relevant research papers related to the research topic. This research helps researchers analyze the recent trend and development in the field of DSS in the context of global information management.

This study investigates organizations' intention to use the industrial metaverse. The unified theory of acceptance and use of technology (UTAUT) is used as an underpinning theory to examine the impact of performance expectancy, effort expectancy, social influence, and facilitation condition on the intention to use the industrial metaverse. The results of this study reveal that performance expectancy, social influence, and facilitating conditions significantly influence the intention to use the industrial metaverse. Moreover, the intention to use the industrial metaverse significantly influences organizational agility and firm performance. Further, the results of moderation hypotheses indicate that the impact of both performance expectancy and social influence on intention to use the industrial metaverse varies at high and low levels of firm innovativeness. The study's findings will enrich the metaverse literature. Further, it provides a deeper understanding of industrial metaverse adoption from a B2B perspective using the underpinnings of UTAUT. The study helps organizations understand the enablers of industrial metaverse usage intention.

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.

Distributed Ledger Technology (DLT) driven blockchain is currently one of the most promising technology revolutions with enormous potential across a wide range of applications. Distributed ledger is essentially a distributed and encrypted database that can address several concerns pertaining to Internet security and trust with transparency. In fact, the scope of blockchain applications is broadening with each passing day. The exclusive features of safe and transparent data exchange provided by blockchain technology provide compelling arguments for its implementation in a variety of application scenarios. In this paper, we present the comprehensive view of blockchain and its related concepts. Though, there exists intensive research on these domains; however, these fields are still dealing with several security issues, data reliability and storage and scalability. Blockchain has emerged as a critical technology that can address these issues through its features like decentralization, transparency, immutability and auditability. Building trust in distributed systems without the need for authority is a technological advancement that can be well leveraged by domain, like, Internet of Things (IoT), cloud and social media. Technologies like IoT and Cloud computing can be perceived as the trivial ones to get benefitted from the blockchain. However, the integration of social media and blockchain can revolutionize the domains, like, content creating and sharing, fake news scourge, trademarking and rights management. In this paper, we illustrate the integration of blockchain with IoT, cloud and social media and the related issues and challenges. Moreover, we also show some of the major research works done in each domain.

The purpose of this study is to provide an overview of distributed denial of service (DDoS) attack detection in intelligent systems. In recent times, due to the endemic COVID-19, the use of intelligent systems has increased. However, these systems are easily affected by DDoS attacks. A DDoS attack is a reliable tool for cyber-attackers because there is no efficient method which can detect or filter it properly. In this context, we analyze different types of DDoS attacks and defense techniques for intelligent systems. For the analysis, we used Scopus databases to collect relevant papers in English between 2014 and 2022. This study makes an important contribution to the field of DDoS attack detection for intelligent systems, providing a comprehensive overview of the field's evolution and current status, as well as a comprehensive, synthesized, and organized summary of various perspectives, definitions, and trends in the field.

With the advent of the 21st century, healthcare systems all around the world are facing challenges at an unprecedented scale. The recent covid-19 outbreak is a glaring example of such challenges. After facing such situations anyone can conclude that there is a need to change the way our current health system works and the recent blockchain technology emerged as a way to bring out this change. From universal health blueprint to medical supply chain management and connecting vetted suppliers, blockchain is making an impact. This paper introduces the concept of blockchain along with its applications with the main focus on healthcare and medical systems.

Smishing is a combined form of short message service (SMS) and phishing in which a malicious text message or SMS is sent to mobile users. This form of attack has come to be a severe cyber-security difficulty and has triggered incredible monetary losses to the victims. Many antismishing solutions for mobile devices have been proposed till date but still, there is a lack of a full-fledged solution. Therefore, this paper proposes an efficient approach that analyzes text content and uniform resource locator (URL) presented in the SMS. We have integrated the URL phishing classifier with the text classifier to improve accuracy as some of the SMS contain the URL with no text or much less text. To find out rare words in a report, depending upon the frequency of term (TF) and the reciprocal of document frequency TF-inverse document frequency (IDF), a weighting framework TF-IDF is used. We have used two data sets for both text as well as for URL phishing classifier and used a synthetic minority oversampling technique to balance the training data. The voting classifier simply merges the findings of each classifier passed into it and predicts the output on the basis of voting. In proposed approach integrating KNN, RF, and ETC can detect smishing messages with a 99.03% accuracy and 98.94% precision rate which is relatively efficient compared with existing ones like SmiDCA model which has the given accuracy of 96.40% using Random Forest classifier in BFSA, Feature-Based it has an accuracy of 98.74% and 94.20% true positive rate and Smishing Detector it shows an overall accuracy of 96.29%.

Adaptation and resilience practitioners lack guidance on how to understand and manage extreme climate risk using the data available. We present a methodological framework to integrate the satellite as well as location based data sets to estimate extreme climate risk. The framework, in detail, has been demonstration using a study carried out to quantify extreme rainfall risks in India incorporating the influence of global (large scale oscillations) as well as local factors (population, infrastructure, economic activity) in a probabilistic model. We use nonstationary extreme value theory along with Bayesian uncertainty analysis to model the time varying influence of oscillations such as El Niño/Southern Oscillation, Indian Ocean Dipole, and North Atlantic Oscillation in augmenting high rainfall risks in 637 districts across 29 states of India. It is found that at least 50% of the districts in 8 out of 29 states are at high risk. Extreme risk is observed in 198 (~31%) and 249 (~39%) districts caused by heavy downpour and extremely long wet spells, respectively. This study provides a framework to identify local implications of global factors and is aimed at supporting policy makers in framing extreme rainfall-induced disaster risk reduction strategies.

In the last few decades, technology advancements have paved the way for the creation of intelligent and autonomous systems that utilize complex calculations which are both time-consuming and central processing unit intensive. As a consequence, parallel processing systems are gaining popularity to enhance overall computer performance. Programmers should be able to efficiently utilize available hardware resources with parallelization in an ideal world. Through the automatic parallelization of sequential code, multithreading can be executed without extra supervision. However, a wide range of software dependencies prevents this from being feasible. An architectural framework for speculative parallelization along with an efficient memory analysis and computational algorithms for the code generation are proposed that can provide optimal performance. Furthermore, a suitable support of hardware design as a runtime library to the proposed architectural framework is presented which can be used to recover misspeculated results during execution to minimize speculative parallelism overhead. The implementation makes use of the Low-Level Virtual Machine compiler infrastructure and is tested on numerous benchmarks, thus making it highly scalable in terms of programming languages and architectures. According to our experimental results, there is significant potential for speedup increase. In comparison to the overall function speedup, that is, geomean speedup of 5.2× approximately when using the proposed architecture without hardware support, the proposed architectural framework and algorithm with hardware support give an average geomean speedup of 7.0× approximately on the given benchmark which is written in C/C++.

Because of the overgrowth of data, especially in text format, the value and importance of multi-label text classification have increased. Aside from this, preprocessing and particularly intelligent feature selection (FS) are the most important step in classification. Each FS finds the best features based on its approach, but we try to use a multi-strategy approach to find more useful features. Evaluating and comparing features’ importance and relevance makes using multiple strategy and methods more suitable than conventional approaches because each feature is measured based on several perspectives. Nevertheless, the ensemble FS merges the final performance results of various methods to take advantage of different methods’ strengths and better classify. In this article, we have proposed an ensemble FS method for multi-label text data (MLTD) for the first time using the order statistics (EMFS) approach. We have utilized four multi-label FS (MLFS) algorithms with various particular performances to achieve a good result. In this method, as one of the most important statistics methods, Order Statistics was used to aggregate the ranks of different algorithms, which is robust against noise, redundant and inessential features. In the end, the performance of EMFS, executing six MLTDs, was evaluated according to six performance criteria (ranking-based and classification-based). Surprisingly, the proposed method was more accurate than others among all used MLTDs. The proposed method has improved by 1.5% compared to other methods. This value is based on the results obtained based on six evaluation criteria and all tested data sets.

With regard to large dimensions of contemporary data sets and restricted computational time of intelligent systems, reducing the dimensions of data sets is necessary. Feature selection is a practical way to remove a set of redundant, irrelevant, and noisy features. In this way, the speed of decision-making procedure will be increased while the accuracy of decisions will be retained. To this end, numerous attentions have been attracted to the topic and consequently, extensive range of methods has been proposed. Regarding the goals of the feature selection concept, the proposed algorithms in this field must be fast and accurate. Therefore, this paper proposes a light meanwhile accurate algorithm to fulfill the mentioned goals. The presented algorithm takes the speed advantage of Whale Optimization Algorithm (WOA) to propose a novel feature selection method for intelligent systems. Moreover, to reach the goal of accuracy, the proposed strategy considers three important fitness objectives, namely, the number of selected features, the accuracy of classification, and information gain. The proposed scheme considers an accurate multiobjective fitness function instead of manipulating the basic algorithm. The reason is that improving the basic algorithms, WOA in our case, may lead to loading more computational complexity. Also, to make the proposed algorithm as light as possible, this paper considers K-nearest neighbor algorithm as the main classifier. The proposed light feature selection algorithm is run on different data sets. Experimental results prove that this algorithm is able to reduce the number of features meanwhile it retains, and in some cases even increases, the accuracy of classification.

The pandemic has shown us that it is quite important to keep track record our health digitally. And at the same time, it also showed us the great potential of Instruments like wearable observing gadgets, video conferences, and even talk bots driven by artificial intelligence (AI) can provide good care from remotely. Real time data collected from different health care devices of cases across globe played an important role in combatting the virus and also help in tracking its progress. The evolution of biomedical imaging techniques, incorporated sensors, and machine learning (ML) in recent years has led in various health benefits. Medical care and biomedical sciences have become information science fields, with a solid requirement for refined information mining techniques to remove the information from the accessible data. Biomedical information contains a few difficulties in information investigation, including high dimensionality, class irregularity, and low quantities of tests. AI is a subfield of AI and computer science which centric the utilization of information and calculations to impersonate the way that people learn, steadily further developing its accuracy. ML is an essential element of the rapidly growing area of information science. Calculations are created using measurable procedures to make characterizations or forecasts, exposing vital experiences inside information mining operations. In this chapter, we explain and compare the different algorithms of ML which could be helpful in detecting different disease at earlier stage. We summarize the algorithms and different steps involved in ML to extract information for betterment of the society which is already exposed to the world of data.

The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers.

The demand for Internet security has escalated in the last two decades because the rapid proliferation in the number of Internet users has presented attackers with new detrimental opportunities. One of the simple yet powerful attack, lurking around the Internet today, is the Distributed Denial-of-Service (DDoS) attack. The expeditious surge in the collaborative environments, like IoT, cloud computing and SDN, have provided attackers with countless new avenues to benefit from the distributed nature of DDoS attacks. The attackers protect their anonymity by infecting distributed devices and utilizing them to create a bot army to constitute a large-scale attack. Thus, the development of an effective as well as efficient DDoS defense mechanism becomes an immediate goal. In this exposition, we present a DDoS threat analysis along with a few novel ground-breaking defense mechanisms proposed by various researchers for numerous domains. Further, we talk about popular performance metrics that evaluate the defense schemes. In the end, we list prevalent DDoS attack tools and open challenges.

Information management collects data from several online systems. They analyze the information. They issue reports about information for supporting decision-making management. Utilizing current modern innovations try to controlling many obstacles such as, high cost, high battery power, and speed system, safety System without building a full system to solve all these problems together, we created a new internet of things ( IoT) system that provides attention to safety, and Security with low cost, low battery power, and high-speed System. As for the information management system. This paper aims at developing an active system for managing most of the smart farm and home obstacles, such issues to deal with the security system for the farm's and house and animal hanger, raining, irrigation and watering system, food supplement system, Also, a network was established to connect all those systems. Connected database storage was used, infra-red, The system is used for monitoring. They send all the collected information back to be maintained. Arduino will be used for programming this system

The global impact of social values, norms, and cultures set the growth and future dimensions of most businesses. In global business governess, the sustainability of social entrepreneurship is heavily dependent on peoples' opinions and their social interactions. Nowadays, social media platforms represent the big global repositories of publically available information that can be exploited by social entrepreneurs to measure and assess the social impact of their business. There is still inadequate research that focuses on assessing social entrepreneurship impact in the area of big data. This paper aims to investigate the potential of big data in global social entrepreneurship. It examines the possibility of global impact of big data in social entrepreneurship. As an outcome, this paper highlight the challenges of social entrepreneurship dealing with, how they tackle globally, big data in social innovation, and how big data analytics needs for social entrepreneurship towards achieving social goods and sustainable change.

Internet of Things (IoT) is playing more and more important roles in our daily lives in the last decade. It can be a part of traditional machine or equipment to daily household objects as well as wireless sensor networks and devices. IoT has a huge potential which is still to be unleashed. However, as the foundation of IoT is the Internet and all the data collected by these devices is over the Internet, these devices also face threats to security and privacy. At the physical or sensor layer of IoT devices the most commonly used technology is RFID. Thus, securing the RFID tag by cryptographic mechanisms can secure our data at the device as well as during communication. This article first discusses the flaws of our previous ultra-lightweight protocol due to its vulnerability to passive secret disclosure attack. Then, the authors propose a new protocol to overcome the shortcomings of our previous work. The proposed scheme uses timestamps in addition to bitwise operation to provide security against de-synchronization and disclosure. This research also presents a security and performance analysis of our approach and its comparison with other existing schemes.

In recent years, contactless transactions have risen rapidly. It includes NFC, MST, contactless cards, and many other payment methods. These payment methods have certain security issues, and attackers are in a regular search for the exploits to break its security. These security issues require proper analysis to secure user data from attackers. This article will discuss the contactless smart cards and payment systems in detail including the techniques used for securing user data and different possible attacks on the technology used for communication. The article also presents some countermeasures to prevent the attack and issues with those countermeasures. In addition, the article includes some future research issues and suggestions to overcome the security issues in contactless payment system.