There is no denying the fact that a human-like understanding of video surveillance by recognizing object or activity poses a significant challenge for autonomous systems in urban areas. In smart cities or environments, autonomous systems and people exist together in shared public spaces. Technology advancements have enabled the machines/ systems to understand or recognize human actions in videos, but accurate and efficient human action recognition is a potential conundrum for the researchers in the field of computer vision. This area is still open for further research to develop systems that can be used productively in a stable and reliable manner. In areas where autonomous systems have to interact with people, it is very important that they have information about what people are exactly doing in their immediate environment. This is especially true if a direct interaction with the human being is to take place. Since human actions are highly
The realm of refrigeration industry has marked its significance for storing food and beverages. Refrigeration warehouses are extensively used to store various combinations of large fresh food stock at favourable temperatures to maintain its quality. Any deviations from these favourable temperatures pose negative impacts on the cold stock. Hence, it is essential to minimize the margin of these potential risks that can incur huge loss to the cold-storage industries. On-site evaluation can be reliable to address these risks, but it is time consuming and an inefficient process as human intervention can be prone to manual errors. The paper, therefore, proposes a methodology to build a smart IoT system to remotely monitor and evaluate the refrigeration warehouses seamlessly at one’s fingertips using a mobile or laptop through dashboards. Furthermore, this paper addresses the difficulties in managing and analysing the large sensory data which are the result of seamless monitoring by unleashing the power of edge computing and cloud computing.
AZ31 magnesium alloy sheets were processed at 250 °C and 300 °C by groove pressing, a severe plastic deformation technique to achieve grain refinement. The influence of processing temperature on the evolution of microstructure, mechanical properties and corrosion behavior was studied. Groove pressing significantly reduced the grain size of the alloy from 46 μm to 6.5 μm at 250 °C processing temperature. With the higher processing temperature (300 °C), grain growth (11.4 μm) was observed for the alloy. Number of twins appeared in the groove pressed samples. Higher hardness and tensile strength were measured for the groove pressed samples processed at 250 °C without significant loss in the ductility as reflected from the % of elongation due to the grain refinement. Corrosion performance of the samples assessed by potentiodynamic polarization studies indicated increased corrosion resistance for both of the grove pressed samples. However, sample at 300 °C exhibited better corrosion resistance compared with the sample processed at 250 °C. This can be understood by considering the effect of higher processing temperature on reducing the crystal imperfections which alters the corrosion behavior.
Cyber security is the major concern in today’s world. Over the past couple of decades, the internet has grown to such an extent that almost every individual living on this planet has the access to the internet today. This can be viewed as one of the major achievements in the human race, but on the flip side of the coin, this gave rise to a lot of security issues for every individual or the company that is accessing the web through the internet. Hackers have become active and are always monitoring the networks to grab every possible opportunity to attack a system and make the best fortune out of its vulnerabilities. To safeguard people’s and organization’s privacy in this cyberspace, different network intrusion detection systems have been developed to detect the hacker’s presence in the networks. These systems fall under signature based and anomaly based intrusion detection systems. This paper deals with using anomaly based intrusion detection technique to develop an automation system to both train and test supervised machine learning models, which is developed to classify real time network traffic as to whether it is malicious or not. Currently the best models by considering both detection success rate and the false positives rate are Artificial Neural Networks(ANN) followed by Support Vector Machines(SVM). In this paper, it is verified that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperforms support vector machine (SVM) technique while classifying network traffic as harmful or harmless. Initially to evaluate the performance of the system, NSL-KDD dataset is used to train and test the SVM and ANN models and finally classify real time network traffic using these models. This system can be used to carry out model building automatically on the new datasets and also for classifying the behaviour of the provided dataset without having to code.
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