Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Comparative Study of Evaluation Indicators for Learning Cities and the Implications

With the development of society,more and more cities are participating in the initiative to build learning cities.Constructing an evaluation indicator system for learning cities to monitor the progress and promote their growth has become increasingly important.This paper analyzes the preliminary framework of the UNESCO Global Learning City Index and R3L+Quality Framework.The comparison is made from the aspects of design philosophy,criteria of indicator,and the cycle of evaluation process.The findings suggest that the construction of an evaluation indicator system should be focused more on the diversity of learning city development,the construction of an evaluation process cycle,and the significance of building cooperative networks.

Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber services.The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning ones.Federated Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacypreserved intelligence in IoT applications.Even though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d setting.FL is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors.This paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d data.PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each class.The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets.The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8%as accuracy improvements.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy management.In smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart grids.In complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research challenge.These challenges can be resolved through various machine learning techniques and data analytics.In this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart grid.The proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution companies.In the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm optimization.The simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

Deep Learning Based License Plate Number Recognition for Smart Cities

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

In recent times,cities are getting smart and can be managed effectively through diverse architectures and services.Smart cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic diseases.With latest developments in digital data collection,AI techniques can be employed for clinical decision making process.On the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the globe.Generally,wearables can be employed in healthcare systems that instigate the development of CVD detection and classification.With this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC technique.The proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data collection.Then,the collected data is saved in cloud server for examination.Followed by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health condition.To accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)technique.In addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of CVD.The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

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.

Privacy Preserving Blockchain with Optimal Deep Learning Model for Smart Cities

Recently,smart cities have emerged as an effective approach to deliver high-quality services to the people through adaptive optimization of the available resources.Despite the advantages of smart cities,security remains a huge challenge to be overcome.Simultaneously,Intrusion Detection System(IDS)is the most proficient tool to accomplish security in this scenario.Besides,blockchain exhibits significance in promoting smart city designing,due to its effective characteristics like immutability,transparency,and decentralization.In order to address the security problems in smart cities,the current study designs a Privacy Preserving Secure Framework using Blockchain with Optimal Deep Learning(PPSF-BODL)model.The proposed PPSFBODL model includes the collection of primary data using sensing tools.Besides,z-score normalization is also utilized to transform the actual data into useful format.Besides,Chameleon Swarm Optimization(CSO)with Attention Based Bidirectional Long Short TermMemory(ABiLSTM)model is employed for detection and classification of intrusions.CSO is employed for optimal hyperparameter tuning of ABiLSTM model.At the same time,Blockchain(BC)is utilized for secure transmission of the data to cloud server.This cloud server is a decentralized,distributed,and open digital ledger that is employed to store the transactions in different methods.A detailed experimentation of the proposed PPSF-BODL model was conducted on benchmark dataset and the outcomes established the supremacy of the proposed PPSFBODL model over recent approaches with a maximum accuracy of 97.46%.

Deep Reinforcement Learning Enabled Smart City Recycling Waste Object Classification

The Smart City concept revolves around gathering real time data from citizen,personal vehicle,public transports,building,and other urban infrastructures like power grid and waste disposal system.The understandings obtained from the data can assist municipal authorities handle assets and services effectually.At the same time,the massive increase in environmental pollution and degradation leads to ecological imbalance is a hot research topic.Besides,the progressive development of smart cities over the globe requires the design of intelligent waste management systems to properly categorize the waste depending upon the nature of biodegradability.Few of the commonly available wastes are paper,paper boxes,food,glass,etc.In order to classify the waste objects,computer vision based solutions are cost effective to separate out the waste from the huge dump of garbage and trash.Due to the recent developments of deep learning(DL)and deep reinforcement learning(DRL),waste object classification becomes possible by the identification and detection of wastes.In this aspect,this paper designs an intelligence DRL based recycling waste object detection and classification(IDRL-RWODC)model for smart cities.The goal of the IDRLRWODC technique is to detect and classify waste objects using the DL and DRL techniques.The IDRL-RWODC technique encompasses a twostage process namely Mask Regional Convolutional Neural Network(Mask RCNN)based object detection and DRL based object classification.In addition,DenseNet model is applied as a baseline model for the Mask RCNN model,and a deep Q-learning network(DQLN)is employed as a classifier.Moreover,a dragonfly algorithm(DFA)based hyperparameter optimizer is derived for improving the efficiency of the DenseNet model.In order to ensure the enhanced waste classification performance of the IDRL-RWODC technique,a series of simulations take place on benchmark dataset and the experimental results pointed out the better performance over the recent techniques with maximal accuracy of 0.993.

Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection

In an urban city,the daily challenges of managing cleanliness are the primary aspect of routine life,which requires a large number of resources,the manual process of labour,and budget.Street cleaning techniques include street sweepers going away to different metropolitan areas,manually verifying if the street required cleaning taking action.This research presents novel street garbage recognizing robotic navigation techniques by detecting the city’s street-level images and multi-level segmentation.For the large volume of the process,the deep learning-based methods can be better to achieve a high level of classifica-tion,object detection,and accuracy than other learning algorithms.The proposed Histogram of Oriented Gradients(HOG)is used to features extracted while using the deep learning technique to classify the ground-level segmentation process’s images.In this paper,we use mobile edge computing to process street images in advance andfilter out pictures that meet our needs,which significantly affect recognition efficiency.To measure the urban streets’cleanliness,our street clean-liness assessment approach provides a multi-level assessment model across differ-ent layers.Besides,with ground-level segmentation using a deep neural network,a novel navigation strategy is proposed for robotic classification.Single Shot Mul-tiBox Detector(SSD)approaches the output space of bounding boxes into a set of default boxes over different feature ratios and scales per attribute map location from the dataset.The SSD can classify and detect the garbage’s accurately and autonomously by using deep learning for garbage recognition.Experimental results show that accurate street garbage detection and navigation can reach approximately the same cleaning effectiveness as traditional methods.

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Intelligent Transportation System(ITS)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality.With the help of big data and communication technologies,ITS offers real-time investigation and highly-effective traffic management.Traffic Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data.Neural Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time.Deep Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction tasks.With this motivation,the current study introduces a novel Slime Mould Optimization(SMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(SMOBGRU-TP)in smart cities.Initially,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization approach.Besides,BiGRUmodel is employed for effective forecasting of traffic in smart cities.Moreover,the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method.The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

北京学习型城市网E-learning系统设计

当前社会飞速发展,知识、技能和观念日新月异,教育形式从面授延伸到网络远程教育。该文主要结合现代远程教育实践发展经验,以北京学习型城市网E-learning系统设计为例,在介绍学习型城市现状的基础上,介绍了如何发挥E-learning优势,迎合学习型城市发展需求、设计原则以及特点,并提出了展望,以期推进学习型城市建设的理论研究和实践发展。

Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

Android devices are popularly available in the commercial market at different price levels for various levels of customers.The Android stack is more vulnerable compared to other platforms because of its open-source nature.There are many android malware detection techniques available to exploit the source code andfind associated components during execution time.To obtain a better result we create a hybrid technique merging static and dynamic processes.In this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing system.In the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given approach.The Android Sensitive Permission is one major key point to be considered while detecting malware.We select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or benign.Thefinal goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.

Teaching and Learning on the Internet： A New Model of University, the International Telematic University UNINETTUNO

The convergence of telecommunications and computer science, the realization of computer-based networks and the integration of languages, by overcoming space and time constraints, gave rise to the globalization process and to the development of the knowledge society. We are facing a true revolution that is based on the multiplication of knowledge and its corresponding applications, but also on the knowledge codification, memorization and knowledge transfer. The challenges that educational institutions, and the University in particular, are called to face are linked to the fact that classrooms or lecture halls are no longer the only places where one can follow study courses： anybody from anywhere, if he has the required technological equipment and the appropriate materials can build his own environment to carry on his own educational and self-learning process. This is the reason why we need to identify new models of university and psycho-pedagogic theories allowing for the development of new Internet-based teaching and learning models by carrying on research work. This paper describes the university model proposed by International Telematic University UN1NETTUNO, rapidly become acknowledged at an international level.

The Role of Deep Learning in Parking Space Identification and Prediction Systems

In today’s smart city transportation,traffic congestion is a vexing issue,and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40%of traffic congestion.Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives,resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space.This explains the need to predict the availability of parking spaces.Recently,deep learning(DL)has been shown to facilitate drivers to find parking spaces efficiently,leading to a promising performance enhancement in parking identification and prediction systems.However,no work reviews DL approaches applied to solve parking identification and prediction problems.Inspired by this gap,the purpose of this work is to investigate,highlight,and report on recent advances inDLapproaches applied to predict and identify the availability of parking spaces.Ataxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature,and by doing so,the salient and supportive features of different DL techniques for providing parking solutions are presented.Moreover,several open research challenges are outlined.This work identifies that there are various DL architectures,datasets,and performance measures used to address parking identification and prediction problems.Moreover,there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain.This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities.This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits,the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.

Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related features.RS has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features.This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these issues.The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities.To attain this,the presented ODLTCP-HRRSI model performs two major processes.At the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic flow.Next,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)algorithm.The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.

An Enhanced Multiview Transformer for Population Density Estimation Using Cellular Mobility Data in Smart City

This paper addresses the problem of predicting population density leveraging cellular station data.As wireless communication devices are commonly used,cellular station data has become integral for estimating population figures and studying their movement,thereby implying significant contributions to urban planning.However,existing research grapples with issues pertinent to preprocessing base station data and the modeling of population prediction.To address this,we propose methodologies for preprocessing cellular station data to eliminate any irregular or redundant data.The preprocessing reveals a distinct cyclical characteristic and high-frequency variation in population shift.Further,we devise a multi-view enhancement model grounded on the Transformer(MVformer),targeting the improvement of the accuracy of extended time-series population predictions.Comparative experiments,conducted on the above-mentioned population dataset using four alternate Transformer-based models,indicate that our proposedMVformer model enhances prediction accuracy by approximately 30%for both univariate and multivariate time-series prediction assignments.The performance of this model in tasks pertaining to population prediction exhibits commendable results.

Landslide-oriented disaster resilience evaluation in mountainous cities: A case study in Chongqing, China

Natural and human-made disasters are threatening cities around the world.The resilience of cities plays a critical role in disaster risk response and post-disaster recovery.In mountainous cities,landslides are among the most frequent and destructive hazards.This study presents a novel methodological framework for assessing the spatial resilience of mountainous cities specifically against landslides.Focusing on Chongqing in the Three Gorges Reservoir region,this study conceptually divides the disaster resilience of mountain cities to landslides into two dimensions:environmental resilience and social resilience.This study developed a comprehensive database by compiling data from 4,464 historical landslide events,incorporating 17 environmental resilience indicators and 16 social resilience indicators.Random forest(RF)model was employed to evaluate environmental resilience,achieving a high AUC of 0.968 and an accuracy of 97.1%.Social resilience was assessed by the Analytic Hierarchy Process(AHP),and comprehensive resilience was ranked by the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).Key findings include:(1)Establishing a multi-dimensional resilience indicator system that effectively assesses landslide-oriented resilience in mountainous cities.(2)Comprehensive resilience in mountainous cities exhibit distinct spatial clustering patterns.Regions with lower environmental resilience are mainly characterized by high rainfall and complex terrain.higher social resilience concentrated in city centers,while peripheral regions face challenges due to weaker economies and inadequate healthcare infrastructure.(3)In the future development of mountain cities,comprehensive and sustainable strategies should be adopted to balance the relationship between environmental resilience and social resilience.This study provides a robust framework for disaster prevention and resilience assessment in mountainous cities,which can be applied to evaluate the disaster resistance capabilities of other mountainous cities.