Aerodinamičan dizajn i analiza motorističke kacige s vizirom protiv odsjaja

The number of motorcycle accidents has increased in the last two decades. Helmet can protect the vehicle riders from severe injuries during road accident to certain extent. To design a functional helmet, it is important to analyse the shape of the helmet and visor portion. Therefore, the attempt has been made to design and analyze new helmet by considering the pressure drag and anti-glare visor. The pressure drag resistance presses the helmet against the neck portion of the rider. The shape of an aerodynamic helmet can reduce the drag pressure. The spherical shape and new aerodynamic shape helmets are designed using Pro-E software. Pressure drag is calculated and comparison is made on the basis of drag pressure.Broj motociklističkih nesreća u posljednja se dva desetljeća povećao. Kaciga u određenoj mjeri može zaštititi motocikliste od teških ozljeda koje je moguće zadobiti tijekom prometne nesreće. Prilikom dizajniranja funkcionalne kacige važno je analizirati oblik kacige i veličinu vizira. Iz tog se razloga pokušalo dizajnirati i analizirati novu kacigu uzimajući u obzir tlak otpora zraka i vizir protiv odsjaja. Tlak otpora zraka pritišće kacigu na vratni dio tijela vozača. Oblik aerodinamične kacige može smanjiti pritisak otpora zraka. Kacige sfernog oblika i novog aerodinamičnog oblika izrađene su pomoću Pro-E software-a. Izračunati su tlakovi otpora zraka za oba oblika kacige i napravljena je usporedba rezultata

Smart Water Resource Management Using Artificial Intelligence—A Review

Water management is one of the crucial topics discussed in most of the international forums. Water harvesting and recycling are the major requirements to meet the global upcoming demand of the water crisis, which is prevalent. To achieve this, we need more emphasis on water management techniques that are applied across various categories of the applications. Keeping in mind the population density index, there is a dire need to implement intelligent water management mechanisms for effective distribution, conservation and to maintain the water quality standards for various purposes. The prescribed work discusses about few major areas of applications that are required for efficient water management. Those are recent trends in wastewater recycle, water distribution, rainwater harvesting and irrigation management using various Artificial Intelligence (AI) models. The data acquired for these applications are purely unique and also differs by type. Hence, there is a dire need to use a model or algorithm that can be applied to provide solutions across all these applications. Artificial Intelligence (AI) and Deep Learning (DL) techniques along with the Internet of things (IoT) framework can facilitate in designing a smart water management system for sustainable water usage from natural resources. This work surveys various water management techniques and the use of AI/DL along with the IoT network and case studies, sample statistical analysis to develop an efficient water management framework

Enhancing Security of Host-Based Intrusion Detection Systems for the Internet of Things

The Internet of Things (IoT) infrastructure enables smart devices to learn, think, speak and perform. The facilities of the IoT devices can be enhanced to support an intelligent application through technologies like fog computing, smart networks, federated learning or explainable artificial intelligence infrastructures. In all these cases networking of IoT devices becomes inevitable. Whereever there exists a network, a threat to the network infrastructure is also possible. The proposed work classifies various attacks on the hosts with the support of proven machine learning (ML) algorithms. This work performs the comparative analysis of all these classification parameters of the machine learning algorithms with the use of fuzzy-based recommendation systems. This work also lists out various incidents of intrusions on the IoT hosts in appropriate layers of the interface and proposes an efficient algorithm and framework to overcome the occurrences of intrusions on the host side. In particular, we propose an effective security framework to deal with the intrusions that can deteriorate the host-based systems. The ranking of the algorithms is evaluated using fuzzy-based recommendation systems such as TOPSIS, VIKOR, MORA, WASPAS. The ensemble of machine learning algorithms such as Decision Tree, Lite Gradient Boost, Xtra Gradient Boost and Random Forest provide better values of accuracy (around 99%) with higher precision, re-call and F1-scores, thus proving their efficacy for intrusion detection in IoT networks