IoT-Enabled Smart City Waste Management using Machine Learning Analytics

2018 ◽  
Author(s):  
Taimur Bakhshi ◽  
Muhammad Ahmed
Keyword(s):  
Machine Learning ◽  
Waste Management ◽  
Smart City ◽  
Learning Analytics

AN ELECTRONICS SOLUTION TO FACILITATE SMART CITY FOR WASTE MANAGEMENT

2019 ◽  
Vol 9 (3) ◽  
pp. 38 ◽  
Author(s):  
M. ADAT DEEPALI ◽  
V. MANE DESHMUKH PRASHANT ◽  
S. K. TILEKAR ◽  
B. P. LAGAONKAR ◽  
◽  
...  
Keyword(s):  
Waste Management ◽  
Smart City

scholarly journals Household Waste Management System Using IoT and Machine Learning

2020 ◽  
Vol 167 ◽  
pp. 1950-1959 ◽  
Author(s):  
Sonali Dubey ◽  
Pushpa Singh ◽  
Piyush Yadav ◽  
Krishna Kant Singh
Keyword(s):  
Machine Learning ◽  
Waste Management ◽  
Management System ◽  
Household Waste ◽  
Waste Management System ◽  
Household Waste Management

Machine learning based accident prediction in secure IoT enable transportation system

2021 ◽  
pp. 1-13 ◽  
Author(s):  
Bhabendu Kumar Mohanta ◽  
Debasish Jena ◽  
Niva Mohapatra ◽  
Somula Ramasubbareddy ◽  
Bharat S. Rawal
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Information Exchange ◽  
Secure Communication ◽  
Intelligent Transportation System ◽  
Transportation System ◽  
Information And Communications Technology ◽  
Classification Models ◽  
Architecture Model ◽  
Accident Severity

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

The role of machine learning analytics and metrics in retailing research

2020 ◽  
Author(s):  
Xin (Shane) Wang ◽  
Jun Hyun (Joseph) Ryoo ◽  
Neil Bendle ◽  
Praveen K. Kopalle
Keyword(s):  
Machine Learning ◽  
Learning Analytics

scholarly journals Sustainable Virtual Reality Patient Rehabilitation Systems with IoT Sensors Using Virtual Smart Cities

2021 ◽  
Vol 13 (9) ◽  
pp. 4716
Author(s):  
Moustafa M. Nasralla
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Virtual Reality ◽  
Time Series Analysis ◽  
Smart City ◽  
Smart Cities ◽  
Rehabilitation Services ◽  
Simulation Scenario ◽  
Common Problems ◽  
Iot Devices

To develop sustainable rehabilitation systems, these should consider common problems on IoT devices such as low battery, connection issues and hardware damages. These should be able to rapidly detect any kind of problem incorporating the capacity of warning users about failures without interrupting rehabilitation services. A novel methodology is presented to guide the design and development of sustainable rehabilitation systems focusing on communication and networking among IoT devices in rehabilitation systems with virtual smart cities by using time series analysis for identifying malfunctioning IoT devices. This work is illustrated in a realistic rehabilitation simulation scenario in a virtual smart city using machine learning on time series for identifying and anticipating failures for supporting sustainability.

Machine learning techniques based on security management in smart cities using robots

Work ◽  
2021 ◽  
pp. 1-12
Author(s):  
Zhang Mengqi ◽  
Wang Xi ◽  
V.E. Sathishkumar ◽  
V. Sivakumar
Keyword(s):  
Machine Learning ◽  
Information And Communication Technologies ◽  
Smart City ◽  
Smart Cities ◽  
Security Management ◽  
Machine Learning Techniques ◽  
Quality Of Services ◽  
Learning Techniques ◽  
Learning Concept

BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects a lot of information and communication technologies that are used to maximize the quality of services. Even though the intelligent city concept provides a lot of valuable services, security management is still one of the major issues due to shared threats and activities. For overcoming the above problems, smart cities’ security factors should be analyzed continuously to eliminate the unwanted activities that used to enhance the quality of the services. OBJECTIVES: To address the discussed problem, active machine learning techniques are used to predict the quality of services in the smart city manages security-related issues. In this work, a deep reinforcement learning concept is used to learn the features of smart cities; the learning concept understands the entire activities of the smart city. During this energetic city, information is gathered with the help of security robots called cobalt robots. The smart cities related to new incoming features are examined through the use of a modular neural network. RESULTS: The system successfully predicts the unwanted activity in intelligent cities by dividing the collected data into a smaller subset, which reduces the complexity and improves the overall security management process. The efficiency of the system is evaluated using experimental analysis. CONCLUSION: This exploratory study is conducted on the 200 obstacles are placed in the smart city, and the introduced DRL with MDNN approach attains maximum results on security maintains.

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