– The infotainment system curves an innovative mechanism of essential information dissemination for ensuring driving safety and improved driving experiences. However, there are significant challenges in developing an infotainment system to fit into the operational design of advanced technology, e.g

/pdf/optimized-controller-scheme-for-autonomous-navigation-in-7w2jb92z.pdf

Optimized Controller Scheme for Autonomous Navigation in Infotainment on Internet-of-Vehicles

The proliferation of cyberattacks has emerged as a significant obstacle for advancing technologies such as the Internet of Things (IoT) and Internet of Vehicles (IoV) in recent times. Notably, cryptographic security measures have been implemented in IoV to counteract these cyberattacks. However, these security measures are inadequate when it comes to thwarting internal attackers within the network, as these attackers possess the necessary security credentials for authenticating basic safety messages (BSMs). The research community has made substantial contributions by proposing misbehavior detection systems (MDS) based on data-centric machine learning to identify and prevent internal attackers within IoV. Nevertheless, the existing MDSs in the literature rely on BSMs received from a single vehicle, thereby enabling internal attackers to manipulate their falsified BSMs and evade detection, resulting in a high incidence of false alarms. In this study, we introduce a new intelligent system for detecting falsified BSMs, employing a trusted neighbor vehicle approach (NIBFADS-UTVA)). Our approach demonstrates exceptional effectiveness, achieving an accuracy, precision, recall, and F1-Score all exceeding 99%. 

pdf/novel-intelligent-bsm-falsification-attack-detection-system-17iotaor5x.pdf

Novel Intelligent BSM Falsification Attack Detection System Using Trusted Neighbor Vehicle Approach in IoV

—Infotainment system potentially contributes towards controlling accident fatalities in the era of Internet-of-Vehicles (IoV). Review of existing system is carried out to find that irrespective of various methods towards infotainment system, the quality of data being retrieved as well as issues associated with power and traffic congestion in vehicular communication is still an impending challenge. Therefore, this manuscript introduces a novel predictive scheme that offers enriched set of information from the environment to assists in decision making. Reinforcement learning is adopted for controlling traffic signal and power while the proposed system introduce augmented Long Short Term Memory scheme in order to predict the best possible traffic scenario for assisting the infotainment system to make a precise decision. The simulation is carried out for proposed system with existing learning schemes to find out proposed scheme offers better performance in every respect over challenging scene of an IoV.

/pdf/framework-of-infotainment-using-predictive-scheme-for-2eyjrchi.pdf

Framework of Infotainment using Predictive Scheme for Traffic Management in Internet-of-Vehicle

International Journal of Pharmaceutical Sciences Review and Research International Journal of Pharmaceutical Sciences Review and Research Available online at www.globalresearchonline.net ©Copyright protected. Unauthorised republication, reproduction, distribution, dissemination and copying of this document in whole or in part is strictly prohibited. Available online at www.globalresearchonline.net 165 Reshma S*, Vinay Kumar D, Srinivas Reddy K Department of Pharmaceutics, Jawaharlal Nehru Technological University, Kakinada, 533003, Andhra Pradesh, India. *Corresponding author’s E-mail: reshmashaik33764@gmail.com

/pdf/self-nanoemulsifying-drug-delivery-system-1hofgiyz.pdf

Self-Nanoemulsifying Drug Delivery System

Hypertension is a major public health problem because of its high ubiquity among around the world. Currently, hypertension in young adults is very common. It is because of the increase in lifestyle behaviors with advancement of technology and better standard of living. The factors responsible for hypertension among young adults are unspecified. The risk of hypertension is very high among young adults as it contributes to an early onset of coronary heart diseases, stroke and ephemeral ischemic attacks. The work mainly focuses on the identification of major facets responsible for hypertension in young adults and to develop a computational model for determining hypertension. The data collection is carried out among young adults in the community through clinical tests. A set of Questionnaires are prepared based on the study to analyze what are the risk factors for hypertension. Statistical analysis is done to identify the major contributing risk factors and Network-Oriented Modeling based on Temporal-Causal Network (TCN) is used for preparing the computational model for hypertension. The novelty of this paper is the usage of Temporal-Causal network modeling, which helps in depicting and cope-with causal dependencies, propagation over time. With the help of Statistical analysis and Network-Oriented Modeling using Temporal-Causal Network, facets contributing hypertension among young adults are identified and the progression of disease is modeled.

A Temporal-Causal Network Model for Hypertension Among Young Adults

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Papers

Optimized Controller Scheme for Autonomous Navigation in Infotainment on Internet-of-Vehicles

Framework of Infotainment using Predictive Scheme for Traffic Management in Internet-of-Vehicle

Self-Nanoemulsifying Drug Delivery System

A Temporal-Causal Network Model for Hypertension Among Young Adults