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The rapid advancement of IoT technology presents transformative opportunities across various sectors, with education being a prominent beneficiary. Smart classrooms, a product of IoT integration, are being widely adopted to create technology-enhanced, student-centric learning environments that cater to students' information literacy needs, particularly during events like pandemics. This widespread adoption generates substantial amounts of educational data, commonly known as big data, necessitating innovative solutions for analysis and utilization. To solve these challenges, this paper proposes utilizing the Apriori algorithm—a data mining technique renowned for uncovering valuable patterns and associations within extensive datasets. This paper evaluates the impact of various information resources with differing quality, considering individuals' information literacy skills. Utilizing data mining techniques, it delves into university students' information literacy data, integrating it with the university library resources to establish a data-driven information literacy education model. It then focuses on criteria, components, and effective methods for instructing college students in information literacy. Finally, a diverse group of students, from first-year undergraduates to doctoral candidates at a specific university, is studied for their engagement in information literacy instruction. Based on the experimental findings, sophomore students exhibited the highest level of participation at 75.9% accuracy, while postgraduate students received more information literacy training than undergraduates and Ph.D. students. When comparing this method to others, such as SVM, KNN, LR, RF, and DT, it achieved superior performance. Additionally, the quality of information literacy training in university libraries was assessed through three dimensions: student learning, behavior, and achievements. Only junior, senior, and first-year graduate students scored above 4, with scores of 4.18, 4.15, and 4.26, respectively. [ABSTRACT FROM AUTHOR]

Internet of things establishes communication among heterogeneous devices. IoT network is low power and lossy network known as LLN. The components in LLN use low power for its operations. The Internet Engineering Task Force (IETF) has defined routing protocol for standardized LLN, i.e., routing protocol for low-power and lossy networks (RPL). One of the major challenges in RPL is efficient conservation of node energy to improve the life of the LLN network. In the RPL network, most of the energy is consumed while regulating and controlling the packets rather than transmission. The algorithm used for regulating and controlling packet in RPL is called trickle timer algorithm. Hence to improve the lifetime of network it is essential to modify the existing trickle timer algorithm. In this paper, we have proposed a new algorithm called EE-trickle. The performance of EE-trickle is compared with existing trickle using the simulator Cooja and using open test bed of future Internet of things lab. From the experiments, it is identified that EE-trickle provides better PDR along with less energy consumption than the existing trickle. Hence, the paper helps the future researchers who work on energy consumption in RPL to make use of EE-trickle in their experiment rather than existing trickle. [ABSTRACT FROM AUTHOR]

The important part of mechanical equipment is rotating machinery, used mostly in industrial machinery. Rolling element bearings are the utmost dominant part in rotating machinery, so even small defects in these components could result in catastrophic system failure and enormous financial losses. Hence, it is crucial to create consistent and affordable condition monitoring and fault diagnosis systems that estimate severity level and failure modes and to create an appropriate maintenance strategy. The studies reveal that the fault diagnostic system focuses on single fault diagnosis of the shaft-bearing system. However, in real scenarios, the occurrence of a single fault is very unlikely. Thus, multifault diagnosis of the shaft-bearing system is of greater significance. This paper aims at steadily and broadly summarizing the development of the intelligent multifault diagnostic and condition monitoring systems. In addition, there is a rapid development of application of Internet of things, cloud computing and artificial intelligence techniques for fault diagnosis. In this paper, we summarize the study of various fault diagnostic system built on the architecture and application of these cutting-edge technologies for predictive maintenance of mechanical equipment. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is an Intelligent Transportation System, which in its turn is an application of the Internet of Things. The IoV is a network of connected vehicles that sends/receives messages. However, the moving nature of the vehicles in IoV networks raises a dynamic topology issue. Therefore, establishing effective and dependable communication pathways among vehicular nodes, contingent upon traffic density conditions, presents a growing challenge. As a result, the main contribution of this paper is to propose an efficient network optimization system involving a cluster-based network routing optimizer. The proposed method is a metaheuristic algorithm adapted as a clustering algorithm. For this purpose, the JAYA Algorithm (JAYA) was utilized and integrated with two clustering concepts extracted from K-means Clustering and Automatic Clustering, resulting in proposing JAYA Clustering Algorithm for IoV (JAYACIoV). Subsequently, the performance of the proposed algorithm was investigated utilizing 176 network testing scenarios, and extensive comparative evaluations were conducted against five algorithms from the literature. The results confirmed the superiority of the proposed system with a percentage of 64.82%, where the algorithms were ranked according to the percentage achievement of best results. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has performed a paradigm shift in the method devices and systems interact, allowing seamless connectivity and communication. But, the enhancing interconnectedness and difficulty of IoT platforms also establish novel security problems, making intrusion detection a vital feature of IoT system security. Intrusion detection systems (IDS) role a vital play in detecting and monitoring unauthorized actions or malicious behavior in IoT networks. Typical IDS frequently depends on handcrafted rules or signatures that cannot not efficiently capture the difficult and developing nature of recent cyber threats. Deep learning (DL) methods automatically study and extract high-level representations in raw data, allowing more accurate and adaptive IDs. Most problems in emerging effectual IDSs are the presence of class imbalances in the database, but the count of normal instances far outweighs the count of intrusion samples. This paper focuses on the design of class imbalance data handling with optimal deep learning-based intrusion detection (CIDH-ODLID) techniques in IoT environments. The purpose of this study is to develop a CIDH-ODLID technique for the identification of intrusions in the IoT platform. For the class imbalance data handling process, the SMOTE approach is used in this work. In addition, the CIDH-ODLID technique employs an echo state network (ESN) approach for intrusion detection and classification. Finally, the snake optimization algorithm (SOA) was carried out for an optimum hyperparameter selection of the ESN approach. The performance validation of the CIDH-ODLID approach was performed on the benchmark database. To prove that the previously provided model performed better, a thorough simulation was run. The researchers provided a thorough comparative analysis that showed the proposed approach was better than other current procedures. It had an accuracy of 99.56%, precision of 97.50%, recall of 98.42%, and an F-score of 97.95%. The experimental outcomes exhibit the promising performance of the CIDH-ODLID technique over other models. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 from Wuhan, China in 2019 has spread across the world, with more than 687 million confirmed coronavirus cases still increasing. Vaccines and medicines are one aspect of fighting the pandemic outbreak, but modern Information Technologies, web/ mobile-based technologies, blockchain, and Internet of Things (IoT) devices are being used to control the pandemic outbreak at an earlier/later stage. The IoT devices with sensors, and mobile apps/ websites having self-assessment tests can help in tracking COVID-19 infections. These people's tracking information and self-assessment tests are generating huge amounts of data. This white paper introduces an IoT and cloud-based architectural framework in the COVID-19 pandemic to predict infection risk based on citizens' self-assessments and storage of data in the cloud and the intelligent technique of hesitant Intuitionistic Fuzzy Set (IFS) has been used on the self-assessment dataset to predict the risk. [ABSTRACT FROM AUTHOR]

In recent years, the convergence of the Internet of Things (IoT) and machine learning technologies has instigated advancements in IoT, and machine learning has revolutionized SMEs, reshaping their role in regional economic growth. These technologies provide SMEs with innovative tools, fostering connectivity within regional ecosystems. This transformation is crucial for China's economic realignment in its 'new normal' phase, with enterprises driving social and economic progress. This study proposed the XGBoost algorithm and IoT data enhance collaboration among SMEs in regional economies, fostering innovation and driving economic development. The role of IoT and machine learning in promoting sustainable regional economic prosperity. Technological innovation is essential for enterprise survival and growth in a competitive landscape. This paper examines the influence of environmental factors and geography on economic development across China's regions. It focuses on SME coupling and coordination, contributing to regional economic progress through performance evaluation. IoT integration provides SMEs with abundant real-time data, enabling deep insights into customer behavior, supply chains, and production. Simultaneously, the XGBoost algorithm efficiently processes and extracts actionable insights from this data. Data from 11 provinces in the Yangtze River economic belt highlights Jiangsu Province's leadership in regional innovation performance for 2015 and 2020. The empirical results, grounded in datasets amalgamating information from these provinces, attest to the potential of this IoT and XGBoost-driven approach. Boasting an impressive accuracy rate of 91.7%, surpassing alternative machine learning techniques like SVM, KNN, DT, RF, and LR, this research underscores the effectiveness of this integrated strategy in optimizing SME operations. Additionally, it computes the coupling coordination degree, innovation environment ranking, mean value, and numerical changes from 2010 to 2020, with an average innovation coupling coordination degree of 0.1624 across 30 Chinese provinces. [ABSTRACT FROM AUTHOR]

The rapid advancement of technology has resulted in significant changes in the field of education. In recent years, the Internet of Things (IoT) and Bayesian network technology have emerged as promising tools for enhancing operations research and teaching performance evaluation. IoT devices and sensors can provide real-time data on student performance, while Bayesian network technology can analyze and predict the complex relationships between various variables. This paper proposes a performance evaluation model based on IoT and Bayesian network technology for operations research teaching. Operations research is a broad discipline with a high level of practicality and applicability. It is widely used in business administration, production planning, traffic management, engineering construction, and financial economics. Using experiments, analysis, and quantification, operations research organizes and manages all types of resources in the system, such as human resources, funds, and goods, and formulates the best business plan. Operations research teaching must combine intuitive geometry and immaterial theorems to improve students' understanding. The methods used in the teaching stage are simple: the teaching content could be more exciting and unintended, and it is difficult to grasp the teaching focus, making accurate evaluation of teaching performance difficult. Therefore, in the Bayesian network, the incremental results and the performance evaluation model of operations research achieve an accuracy of 87.3%. By thoroughly examining the fundamentals of Bayesian network technology, one can offer technical assistance in building an operational research teaching performance evaluation model. To combine with real-world teaching scenarios to assess the likelihood of risky events occurring during instruction and make a teaching performance evaluation model grounded in operational research. The B5 teaching effect has the highest risk probability of teaching work (0.89), and teachers with high professional titles have higher average values. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new revolution that integrates multiple technologies to improve our lifestyle and healthcare. With the use of this technology, football players may be safeguarded from health issues such as a cardiopulmonary respiratory system that could arise during a game or practice. This paper investigates the effect of inhalable particle pollution on the number of cardiovascular and respiratory diseases during outdoor football games using an advanced IoT-enabled approach. First, this research work investigates the negative impacts of inhalable air particles on the health of IoT-enabled outdoor football players. Second, it designs a sophisticated IoT-based monitoring system for real-time health evaluation, with a particular emphasis on the impact of inhalable particle pollution on the cardiovascular and respiratory systems of the participants. This proposed system is made up of three interconnected layers: the Sensory Layer, which is outfitted with lightweight IoT sensors and meticulously stationed for real-time monitoring of the players' physiological parameters and stadium environmental conditions; the Network Layer, which is in charge of facilitating seamless data flow between the sensors and the Cloud Layer; and the Cloud Layer, which is in charge of secure data storage, intricate analysis, and the extraction of invaluable information. Third, it employs Kriging estimation as a multi-step method that includes variogram modeling to analyze spatial data dependency, providing insights into how data points relate to distance and direction. Kriging calculates the difference between data points and generates spatial concentration maps that illustrate the geographical distributions of particle pollutants. Finally, it employs Linear Regression (LR) analysis to investigate the relationship between inhalable particle pollution and cardiopulmonary respiratory disease in outdoor football players. The data are dimensioned before comparison to produce the correlation coefficient, which reveals the relationship between these variables. The experimental result shows that in the single pollution model of inhalable particulate pollutant PM2.5, the relative risk values of cardiorespiratory diseases in different populations of outdoor football are 1.0022, 1.0019, 1.0024, 1.0025, and 1.0028, respectively. In CO, SO2, NO2, O3, and other multi-pollutant models, the concentration level of inhalable particulate pollutant PM2.5 increased by 10 μg/m3, and the number of visits for cardiorespiratory diseases in the whole population increased by 0.339% (95% CI 0.220–0.458) after the 10th day of outdoor football. In addition, the number of visits for cardiorespiratory diseases in men and women who lagged on the 10th day of outdoor football increased by 0.322% (95% CI 0.198–0.446) and 0.345% (95% CI 0.223–0.467), respectively. The number of visits for cardiorespiratory diseases of the elderly and children who lagged on the 10th day of outdoor football increased by 0.373% (95% CI 0.234–0.512) and 0.364% (95% CI 0.229–0.500), respectively. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has significantly impacted international trade, benefiting national and international economies. It has increased trade and financial liberalization in many countries. Ecological and environmental protection have become critical issues due to the fast mobility of money and commodities. The purpose of this paper is to find a way to balance foreign trade growth with environmental protection. We aim to achieve this by using IoT to establish a co-integration model that takes into account the comprehensive environmental index, imports and exports, and the capital-labor ratio. We have collected data from Quanzhou City between 2011 and 2019, based on various environmental pollution indicators, import and export trade volume, capital-labor ratio, and other factors. Our analysis includes an examination of the empirical model of foreign trade and the ecological environment, as well as the impulse response. Additionally, we used prediction variance decomposition and the Granger causality test to understand the coordinated relationship between foreign trade and the ecological environment. Our results demonstrate that when the volume of exports (EX) increases by 1 percent, the ecological environment in Quanzhou City is negatively impacted by about 0.06%. On the other hand, when the volume of imports (IM) increases by 1 percent, there is a 0.26% positive effect on the ecological environment of the city. Finally, when the capital-to-labor ratio (K) increases by 1 percent, there is a 0.40% deterioration of the city's ecological environment. [ABSTRACT FROM AUTHOR]

In recent years, transformative technological advances have reshaped countless sectors, including higher education, leaving a lasting and indelible mark. Artificial intelligence (AI) and the Internet of Things (IoT) coming together has proven to be an influential driving force for the development of creative solutions to the ever-evolving challenges faced by educational institutions. Traditional classroom teaching methods have encountered unprecedented hurdles with the widespread integration of the Internet into education. China's commitment to "governing the nation by law" has elevated the need for legal expertise. As society evolves, pedagogical approaches must adapt. Information and communication technology trends, combined with the rise of the IoT, are reshaping education and offering tools to bolster campus security, monitor vital assets, and enrich classroom accessibility. This paper proposes an AI-based hybrid model and an IoT-infused teaching approach personalized for legal education in colleges and universities, employing the decision tree algorithm. The study's focal point is the development of legal professionals possessing the robust capabilities and qualities society seeks, using teaching methods compatible with modern information technology. The foundation of this endeavor lies in the hybrid teaching mode, specifically Internet-based legal research. Within this platform, educators can disseminate instructional materials, videos, and exercises, with the option to enlist teaching assistants to aid in managing student learning. Comparing varied and traditional teaching classes, the passing rates were 96.75% and 95.36%, respectively. Data analysis using decision trees identifies factors influencing these rates. The study also evaluates students' accuracy in five in-class exercises; in the mixed class, correctness rates were 96.4%, 93.5%, 82.5%, 64.2%, and 62.8%. The proposed method is compared with other machine learning algorithms (SVM, KNN, LR, RF) and Decision Trees to evaluate the educational impact of the mixed teaching approach comprehensively. [ABSTRACT FROM AUTHOR]