Artificial Intelligence Applications to Smart City and Smart Enterprise

Harnessing probabilistic neural network with triple tree seed algorithm-based smart enterprise quantitative risk management framework

Enterprise risk management (ERM) frameworks convey vital principles that help create a consistent risk management culture, irrespective of employee turnover or industry standards. Enterprise Management System (EMS) are becoming a popular research area for assuring a company's long-term success. Statistical pattern recognition, federated learning, database administration, visualization technology, and social networking are all used in this field, which includes artificial intelligence (AI), data science, and statistics. Risk assessment in EMS is critical for enterprise decision-making to be effective. Recent advancements in AI, machine learning (ML), and deep learning (DL) concepts have enabled the development of effective risk assessment models for EMS. This special issue seeks groundbreaking research articles that showcase the application of applied probability and statistics to interdisciplinary studies. This study offers Improved Metaheuristics with a Deep Learning Enabled Risk Assessment Model (IMDLRA-SES) for Smart Enterprise Systems. Using feature selection (FS) and DL models, the provided IMDLRA-SES technique estimates business risks. Preprocessing is used in the IMDLRA-SES technique to change the original financial data into a usable format. In addition, an FS technique based on oppositional lion swarm optimization (OLSO) is utilized to find the best subset of features. In addition, the presence or absence of financial hazards in firms is classified using the triple tree seed algorithm (TTSA) with a probabilistic neural network (PNN) model. The TTSA is used as a hyperparameter optimizer to improve the efficiency of the PNN-based categorization. An extensive set of experimental evaluations is performed on German and Australian credit datasets to illustrate the IMDLRA-SES model's improved performance. The performance validation of the IMDLRA-SES model portrayed a superior accuracy value of 95.70% and 96.09% over existing techniques.

General Industrial Environment and Health Design Software Using a Small Data-Driven Neural Network Model

The developed enterprise intellectual brain neural network platform for industrial environment and health design driven by small data converts the wisdom knowledge of enterprise designers into data and helps enterprises retain design experience, accumulate design knowledge results, sort out the design process, and shorten the design cycle. The goal of this project is to combine artificial intelligence, big data, the Internet of things, parameterization, and other computer technologies with the industrial environment and health design and development process of manufacturing enterprises to solve pain points for businesses and designers, as well as to develop and create a large-scale product-level general parameter intelligent design software. The development of this software project can greatly improve the work efficiency of designers, save the time spent by designers on low-end repetitive labor, and in turn promote designers to engage in more valuable creative work and realize the digitization of entire product life cycle. It can also improve the inefficient and redundant work efficiency of designers, and designers can switch between multiple roles. At the same time, a design think tank is formed through the accumulation of enterprise design knowledge through data, and the neural network platform development of big data drives the design brain, that is, an efficient and intelligent industrial environment and health design expert system is generated, just like the enterprise brain, including expert think tanks, internal technical data encryption of enterprises interface, enterprise core technology think tank, enterprise production resources, risk control standard library, etc.

Machine learning techniques in internet of UAVs for smart cities applications

Recently, there were much interest in technology which has emerged greatly to the development of smart unmanned systems. Internet of UAV (IoUAV) enables an unmanned aerial vehicle (UAV) to connect with public network, and cooperate with the neighboring environment. It also enables UAV to argument information and gather data about others UAV and infrastructures. Applications related to smart UAV and IoUAV systems are facing many impairments issues. The challenges are related to UAV cloud network, big data processing, energy efficiency in IoUAV, and efficient communication between a large amount of different UAV types, in addition to optimum decisions for intelligence. Artificial Intelligence (AI) technologies such as Machine Learning (ML) mechanisms enable to archives intelligent behavior for unmanned systems. Moreover, it provides a smart solution to enhance IoUAV network efficiency. Decisions in data processing are considered one of the most problematic issues related to UAV especially for the operations related to cloud and fog based network levels. ML enables to resolve some of these issues and optimize the Quality of UAV network experience (QoE). The paper provides theoretical fundamentals for ML models and algorithms for IoUAV applications and recently related works, in addition to future trends.

Intelligent Street Lighting in a Smart City Concepts—A Direction to Energy Saving in Cities: An Overview and Case Study

The aim of the article is to present and analyze the implementation of intelligent lighting within the concept of smart energies and smart cities. Motivation and research hypothesis: Electricity consumption in the world is based largely on non-renewable energy. Until these full changes, it is necessary to look for opportunities to save and use it efficiently. Today’s cities are increasingly implementing the smart concept, of which smart energy is one area. One of the smart city elements implemented by cities is smart energy. Within this framework, a supported concept is the replacement of traditional lighting with LEDs (Light-Emitting Diodes), which contributes to energy and cost savings and reduces the pollution of the sky with artificial light, while increasing the efficiency of urban lighting. Positive effects of modern solutions include reduced lighting expenses and increased safety of residents. Methods and results of the research: The authors chose the case study method for their research. The authors present forecasts for the development, not only from the point of view of the number of lamps but also the cost efficiency pointing out the importance of this element in the context of building smart cities. These are specific benchmarks for cities that have not yet implemented this concept. Conclusions and interdisciplinary implications: solutions are desirable directions for the development of the smart city concept, bringing benefits and reducing external costs. Considerations show a quantitative development forecast and an indication of the possibility of achieving trade-offs and cost reductions. It translates into meeting the requirements of sustainable development providing tangible benefits. The analysis of the case studies is intended to show the effects that can be achieved and the wide range of applications (indicating that modern lamps are not just lighting, but a platform for urban services). The analyses presented are intended to serve as benchmarks showing the possibilities of reducing costs and increasing the quality of life of residents in modern cities. The indicated examples and analyses are of economic importance, they show managers, but also inhabitants, in which direction they should lead the smart city concept, which will allow saving costs, but also to increase the quality of life of inhabitants.

Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of the internet of things (IoT) nodes deployed in smart cities. In smart cities, the major IoT applications are smart traffic monitoring, smart waste management, smart buildings and patient healthcare monitoring. The small size IoT nodes based on low power Bluetooth (IEEE 802.15.1) standard and wireless sensor networks (WSN) (IEEE 802.15.4) standard are generally used for transmission of data to a remote location using gateways. The WSN based IoT (WSN-IoT) design problems include network coverage and connectivity issues, energy consumption, bandwidth requirement, network lifetime maximization, communication protocols and state of the art infrastructure. In this paper, the authors propose machine learning methods as an optimization tool for regular WSN-IoT nodes deployed in smart city applications. As per the author’s knowledge, this is the first in-depth literature survey of all ML techniques in the field of low power consumption WSN-IoT for smart cities. The results of this unique survey article show that the supervised learning algorithms have been most widely used (61%) as compared to reinforcement learning (27%) and unsupervised learning (12%) for smart city applications.