Arithmetic Optimization with Deep Learning Enabled Anomaly Detection in Smart City

In recent years,Smart City Infrastructures(SCI)have become familiar whereas intelligent models have been designed to improve the quality of living in smart cities.Simultaneously,anomaly detection in SCI has become a hot research topic and is widely explored to enhance the safety of pedestrians.The increasing popularity of video surveillance system and drastic increase in the amount of collected videos make the conventional physical investigation method to identify abnormal actions,a laborious process.In this background,Deep Learning(DL)models can be used in the detection of anomalies found through video surveillance systems.The current research paper develops an Internet of Things Assisted Deep Learning Enabled Anomaly Detection Technique for Smart City Infrastructures,named(IoTAD-SCI)technique.The aim of the proposed IoTAD-SCI technique is to mainly identify the existence of anomalies in smart city environment.Besides,IoTAD-SCI technique involves Deep Consensus Network(DCN)model design to detect the anomalies in input video frames.In addition,Arithmetic Optimization Algorithm(AOA)is executed to tune the hyperparameters of the DCN model.Moreover,ID3 classifier is also utilized to classify the identified objects in different classes.The experimental analysis was conducted for the proposed IoTADSCI technique upon benchmark UCSD anomaly detection dataset and the results were inspected under different measures.The simulation results infer the superiority of the proposed IoTAD-SCI technique under different metrics.

China＇s Sponge City construction： A discussion on technical approaches

Since 2014, China has been implementing the Sponge City Construction initiative, which represents an enormous and unprecedented effort by any government in the world for achieving urban sustainability. According to preliminary estimates, the total investment on the Sponge City Plan is roughly 100 to 150 million Yuan （RMB） （$15 to $22.5 million） average per square kilometer or 10 Trillion Yuan （RMB） （$1.5 Trillion） for the 657 cities nationwide. The Sponge City Plan （SCP） calls for the use of natural processes such as soil and vegetation as part of the urban runoff control strategy, which is similar to that of low impact development （LID） and green infrastructure （G1） practices being promoted in many parts of the world. The SCP includes as its goals not only effective urban flood control, but also rainwater harvest, water quality improvement and ecological restoration. So far, the SCP implementation has encountered-some barriers and challenges due to many factors. The present paper presents a review of those barriers and challenges, oftizrs discussions and recommendations on several technical aspects such as control goals and objectives; planning/design and construction of LID/GI practices; performance evaluation. Several key recommendations are proposed on Sponge City implementation strategy, Site-specific regulatory fi＇amework and technical gmdance, Product innovation and certification, LID/GI Project financing, LID/G1 profcssional training and certification, public outreach and education. It is expected that the successful implemen!atiun of the. SCP not only will bring about a sustainable, eco-friendly urbanization process in China, but also contribute enormously to the LID/Gl research and development with the vast amount of relevant data and experiences generated from the Sponge City construction projects.