This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Eastern-European Journal of Enterprise Technologies
Security issues and Internet of Things (IoT) risks in several areas are growing steadily with the... more Security issues and Internet of Things (IoT) risks in several areas are growing steadily with the increased usage of IoT. The systems have developed weaknesses in computer and memory constraints in most IoT operating systems. IoT devices typically cannot operate complicated defense measures because of their poor processing capabilities. A shortage of IoT ecosystems is the most critical impediment to developing a secured IoT device. In addition, security issues create several problems, such as data access control, attacks, vulnerabilities, and privacy protection issues. These security issues lead to affect the originality of the data that cause to affects the data analysis. This research proposes an AI-based security method for the IoT environment (AI-SM-IoT) system to overcome security problems in IoT. This design was based on the edge of the network of AI-enabled security components for IoT emergency preparedness. The modules presented detect, identify and continue to identify the ...
Driver behavior is a determining factor in more than 90% of road accidents. Previous research reg... more Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/...
Driver behavior is a concerning issue in the area of intelligent transportation system (ITS). Dri... more Driver behavior is a concerning issue in the area of intelligent transportation system (ITS). Driver behavior is a significant key player in a wide range of unpleasant events during the ride, such as accidents or crashes, traffic congestion, harsh braking, and acceleration/deceleration. Influencing factors of driver behavior have been explored in several studies. It is imperative to investigate these factors in order to provide a comprehensive analysis and to categorize them on the basis of a coherent taxonomy. With that, this study conducted a systematic review on prior studies that focused on bus driver behavior, particularly in the ITS. This study also established a taxonomy on the topic of driver behavior in multiple areas of ITS and their classifications. Different databases, namely ScienceDirect, Web of Science, and IEEE Explore, were utilized to obtain relevant articles from 2008 to 2021 (15 April). Several filtering and scanning stages were performed according to the exclusion/inclusion criteria on all 2,803 articles obtained; however, only 87 articles met the criteria. The final set of articles were categorized into a taxonomy. The first part of the taxonomy focuses on five main factors that influence driver behavior: environmental, demographic, habit, vehicle, and on-road routine factors. The second part of the taxonomy discusses the mapping of data collection methods on the basis of four categories: real-time data collection, survey, simulation, and benchmark. Discussion and analysis were provided to highlight the critical literature gaps on bus driver behavior in the ITS, involving the use of real-time data collection, which is imperative for acquiring highly accurate and sophisticated data. This multi-field systematic review has exposed new research opportunities, motivations, challenges, limitations, and recommendations and highlighted the need for the synergistic integration of interdisciplinary works. Overall, this study presented pathways solution in future direction on the basis of three sequenced phases, namely design, labeling and validation, and machine learning. This study can serve as a guide for future researchers, as it addressed the ambiguities in the ITS-driver behavior domain and provided valuable information on these ITS-driver behavior trends. INDEX TERMS Intelligent transportation system, ITS, bus, behavior, driver behavior, bus driver.
The production of B mesons in Z decays has been measured at LEP with the L3 detector. A sample of... more The production of B mesons in Z decays has been measured at LEP with the L3 detector. A sample of Z → bb events was obtained by tagging muons in 1.6 million hadronic Z decays collected in 1991, 1992 and 1993. A signal with a peak value of E γ = 46. 3 ± 1. 9 (stat) MeV in the B rest frame energy spectrum was interpreted to come from the decay B → γ B. The inclusive production ratio of B mesons relative to B mesons was determined from a fit to the spectrum to be N B / (N B + N B) = 0. 76 ± 0. 08 ± 0. 06, where the first error is statistical and the second is systematic.
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