This solution effectively analyzes driving behaviors, offering recommendations for corrective actions to achieve safe and efficient driving. Fuel consumption, steering dependability, velocity stability, and braking protocols are employed by the proposed model to categorize drivers into ten distinct classes. Through the OBD-II protocol, data from the engine's internal sensors is used in this research, thus eliminating the requirement for any further sensors. Data collection is instrumental in building a driver behavior classification model, yielding feedback for better driving habits. To categorize drivers, key driving events, including high-speed braking, rapid acceleration, deceleration, and turning maneuvers, are considered. To compare the performance of drivers, visualization techniques, like line plots and correlation matrices, are frequently used. The model considers the sensor data's values across time. Supervised learning methods are implemented to conduct a comparative analysis of all driver classes. The SVM, AdaBoost, and Random Forest algorithms achieved accuracies of 99%, 99%, and 100%, respectively. The proposed model features a practical methodology for reviewing driving practices and proposing the appropriate modifications to maximize driving safety and efficiency.
As data trading's market share expands, the risks surrounding identity verification and authority management are becoming increasingly severe. Facing challenges of centralized identity authentication, dynamic identity changes, and ambiguous trading permissions in data trading, a novel two-factor dynamic identity authentication scheme is proposed, leveraging the alliance chain (BTDA). Identity certificates are now easier to utilize, alleviating the challenges posed by demanding calculations and difficult storage. antitumor immune response Another key component involves a dynamic two-factor authentication system, built on a distributed ledger, for authenticating identities dynamically throughout the data trading platform. Bio ceramic Finally, a simulated experiment is performed on the devised plan. Similar schemes were compared and analyzed theoretically, showcasing that the proposed scheme exhibits cost-effectiveness, enhanced authentication efficiency and security, user-friendly authority management, and suitability for various data trading settings.
A multi-client functional encryption method [Goldwasser-Gordon-Goyal 2014] for set intersection allows an evaluator to determine the intersecting elements across a fixed number of clients' data sets without needing access to the individual clients' data sets. Implementing these methodologies renders the calculation of set intersections from random client subsets impossible, consequently narrowing the scope of their utility. selleck To facilitate this option, we redefine the syntax and security paradigms of MCFE schemes, and introduce adaptable multi-client functional encryption (FMCFE) schemes. The aIND security of MCFE schemes is straightforwardly extended to the aIND security of FMCFE schemes. We propose an FMCFE construction, which guarantees aIND security, for a universal set having a polynomial size relative to the security parameter. In O(nm) time, our construction calculates the set intersection for n clients, each of whom holds a set containing m elements. The security of our construction is verified under the DDH1 assumption, a variant of the symmetric external Diffie-Hellman (SXDH) assumption.
A variety of methods have been deployed in an attempt to resolve the difficulties in the automated detection of emotion from text, drawing on established deep learning architectures like LSTM, GRU, and BiLSTM. Unfortunately, these models are constrained by the need for extensive datasets, substantial computational infrastructure, and prolonged training. Additionally, they often display forgetfulness and perform poorly on restricted data samples. This paper examines the effectiveness of transfer learning in grasping the nuanced contextual meanings within text, thereby achieving better emotional recognition, even when faced with constraints in data volume and training duration. To measure effectiveness, we pitted EmotionalBERT, a pre-trained model derived from the BERT architecture, against RNN models on two standard benchmarks. The key variable examined is the amount of training data and its effects on the performance of each model.
High-quality data are essential for decision-making support and evidence-based healthcare, especially when crucial knowledge is absent or limited. Accurate and readily available COVID-19 data reporting is essential for public health practitioners and researchers. COVID-19 data reporting mechanisms exist in every nation, but their overall performance has not undergone a comprehensive evaluation. Although other concerns exist, the current COVID-19 pandemic has revealed widespread shortcomings in data quality standards. For a critical assessment of COVID-19 data reported by the World Health Organization (WHO) in the six Central African Economic and Monetary Community (CEMAC) countries from March 6, 2020 to June 22, 2022, we propose a data quality model based on a canonical data model, four adequacy levels, and Benford's law, and propose potential solutions. The level of data quality sufficiency, considered in relation to the comprehensiveness of Big Dataset examination, provides valuable insights into dependability. Big data analytics' input data quality was effectively ascertained using this model. Scholars and institutions across all sectors must enhance their comprehension of this model's fundamental principles to facilitate its future evolution, fostering integration with existing data processing tools and expanding its practical applications.
Social media's consistent expansion, along with unconventional web technologies, mobile applications, and Internet of Things (IoT) devices, places a strain on cloud data systems, necessitating the handling of extensive datasets and a rapid influx of requests. Data store systems frequently incorporate NoSQL databases, such as Cassandra and HBase, and relational SQL databases with replication, such as Citus/PostgreSQL, to optimize horizontal scalability and high availability. This paper presents an evaluation of three distributed database systems, relational Citus/PostgreSQL and NoSQL databases Cassandra and HBase, on a low-power, low-cost cluster of commodity Single-Board Computers (SBCs). The cluster, composed of fifteen Raspberry Pi 3 nodes, utilizes Docker Swarm for orchestrating service deployment and ingress load balancing across single-board computers (SBCs). Our conclusion is that a budget-friendly cluster of single-board computers (SBCs) possesses the capacity to uphold cloud objectives like horizontal scalability, flexibility, and high reliability. Clear experimental evidence underscored a trade-off between performance and replication, which is essential for system availability and the capability of withstanding network divisions. Furthermore, these two characteristics are indispensable within the framework of distributed systems employing low-power circuit boards. Better results were observed in Cassandra when the client specified its consistency levels. Consistency is a feature of both Citus and HBase, but this benefit is accompanied by a performance reduction as replicas multiply.
Unmanned aerial vehicle-mounted base stations (UmBS) are a promising means to reinstate wireless service in regions devastated by natural events such as floods, thunderstorms, and tsunami strikes, owing to their adaptability, cost-effectiveness, and speedy deployment. The deployment of UmBS, however, presents major challenges, including the precise positioning of ground user equipment (UE), optimization of UmBS transmit power, and the effective pairing of UEs with UmBS. Our paper introduces the LUAU approach, aiming for both ground UE localization and energy-efficient UmBS deployment, accomplished through a method that links ground UEs to the UmBS. Departing from existing research utilizing known UE positions, this work introduces a three-dimensional range-based localization (3D-RBL) method for precisely calculating the geographical positions of ground-based user equipment. An optimization problem is subsequently presented, intending to maximize the user equipment's average data rate by adjusting the transmit power and strategic placement of the UmBS, while accounting for interference stemming from neighboring UmBSs. The Q-learning framework's exploration and exploitation capabilities are employed to attain the optimization problem's objective. The proposed methodology's effectiveness is quantified through simulation, showing its superiority over two benchmark schemes in terms of the UE's mean data rate and outage percentage.
Following the 2019 emergence of the coronavirus (subsequently known as COVID-19), a global pandemic ensued, profoundly altering numerous aspects of daily life for millions. A substantial contribution to the eradication of the disease came from the remarkably swift development of vaccines, accompanied by the strict implementation of preventative measures such as lockdowns. Hence, the worldwide rollout of vaccines was vital for maximizing the immunization of the entire population. However, the rapid advancement of vaccines, compelled by the intention of managing the pandemic, led to a significant display of skepticism among the general public. Added to the existing obstacles in confronting COVID-19 was the public's uncertainty about vaccination. To resolve this problematic situation, it is critical to understand the sentiments of the public about vaccines, thereby facilitating the implementation of appropriate actions to improve public education. Undeniably, people frequently modify their expressed feelings and emotions on social media, thus a thorough assessment of these expressions becomes imperative for the provision of reliable information and the prevention of misinformation. Furthermore, sentiment analysis, as detailed by Wankhade et al. (Artif Intell Rev 55(7)5731-5780, 2022), provides insights. The powerful natural language processing technique, 101007/s10462-022-10144-1, is adept at identifying and classifying people's emotions, primarily within textual data.