ABSTRAT:
The manuscript represents a comprehensive and systematic literature review on the machine learning methods in the emerging applications of the smart cities. Application domains include the essential aspects of the smart cities including the energy, healthcare, transportation, security, and pollution. The research methodology presents a state-of-the-art taxonomy, evaluation and model performance where the ML algorithms are classified into one of the following four categories: decision trees, support vector machines, artificial neural networks, and advanced machine learning methods, i.e., hybrid methods, ensembles, and Deep Learning. The study found that the hybrid models and ensembles have better performance since they exhibit both a high accuracy and low overall cost. On the other hand, the deep learning (DL) techniques had a higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference was negligible, it can be concluded that using either of them is appropriate
EXISTING SYSTEM :
It is challenging to search and identify all studies in which ML algorithms have supported smart cities due to the abundance of such algorithms and their variations. The simple search queries for ‘‘smart city’’ and ‘‘machine learning’’ may not provide a comprehensive list of relevant literature. The search phrase ‘‘smart city’’ is not the only one that we would solely bank on because other search phrases that bear close semantics, such as ‘‘intelligent city,’’ ‘‘smart urban planning,’’ ‘‘smart urban mobility,’’ etc., should not be neglected. The complexity notably increases when we compound the query with the names of many ML algorithms. We relied on the main algorithms discussed in textbooks and in surveys such as [30] for the names of the ML algorithms. In this research, the Scopus database has been used as the primary repository as it indexes the major authenticated publishers
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
the relationship between AI and smart cities and proposed ML techniques for real-time city functions. Mohammadi and Al-Fuqaha [26] shed light on the challenge of big data in smart city applications from a machine learning point of view. The study focused on deep reinforcement learning and how it was used to handle the cognitive aspect of smart city services. Bhattacharya et al. [27] developed a qualitative study for discussing the future of DL-based techniques for smart city applications. Kolovos’s and Anagnostopoulos [28] studied the application of deep reinforcement learning and clustering for query controller application in smart cities as a comparative analysis. Table 1 presents the study’s strengths and weaknesses to generate the central research gap. This table compares the conducted studies with the criteria of the present study. Despite the abundance of the conducted studies, they still have shortcomings and limitations that warrant further investigation and study. Specifically, they do not provide a classification for the ML and DL techniques used or do not categorize their roles and functionality in smart cities. In addition, researchers in the field may be challenged by the scarcity of reviews that contrast the performance of ML techniques and analyze their suitability to solve different problems. Currently the literature lacks a comprehensive review that categorizes ML algorithms and their applications to smart cities. Such a study would guide researchers in the field of smart cities to use the right tool for a given problem. Managing a significant amount of data in review articles can help in the successful implementation of smart cities for future planning and policy-making [29]. Our analysis in this study bridges the gap by providing a taxonomy of the ML algorithms and their contributions to improving smart cities. Furthermore, we provide a quantitative analysis of the performance of these ML algorithms to select the most likely effective one in a given field. We evaluate these algorithms concerning efficiency, accuracy, and computa- tional complexity
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
