scholarly journals Urban living environment assessment index system based on psychological security

2019 ◽  
Author(s):  
Xia Huiyi ◽  
◽  
Nankai Xia ◽  
Liu Liu ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Collection ◽  
Urban Environment ◽  
Evaluation System ◽  
Living Environment ◽  
Mobile Internet ◽  
Practical Significance ◽  
Human Beings ◽  
Urban Living

With the development of urbanization and the continuous development, construction and renewal of the city, the living environment of human beings has also undergone tremendous changes, such as residential community environment and service facilities, urban roads and street spaces, and urban public service formats. And the layout of the facilities, etc., and these are the real needs of people in urban life, but the characteristics of these needs or their problems will inevitably have a certain impact on the user's psychological feelings, thus affecting people's use needs. Then, studying the ways in which urban residents perceive changes in the living environment and how they perceive changes in psychology and emotions will have practical significance and can effectively assist urban management and builders to optimize the living environment of residents. This is also the long-term. One of the topics of greatest interest to urban researchers since then. In the theory of demand hierarchy proposed by American psychologist Abraham Maslow, safety is the basic requirement second only to physiological needs. So safety, especially psychological security, has become one of the basic needs of people in the urban environment. People's perception of the psychological security of the urban environment is also one of the most important indicators in urban environmental assessment. In the past, due to the influence of technical means, the study of urban environmental psychological security often relied on the limited investigation of a small number of respondents. Low-density data is difficult to measure the perceptual results of universality. With the leaping development of the mobile Internet, Internet image data has grown geometrically over time. And with the development of artificial intelligence technology in recent years, image recognition and perception analysis based on machine learning has become possible. The maturity of these technical conditions provides a basis for the study of the urban renewal index evaluation system based on psychological security. In addition to the existing urban visual street furniture data obtained through urban big data collection combined with artificial intelligence image analysis, this paper also proposes a large number of urban living environment psychological assessment data collection strategies. These data are derived from crowdsourcing, and the collection method is limited by the development of cost and technology. At present, the psychological security preference of a large number of users on urban street images is collected by forced selection method, and then obtained by statistical data fitting to obtain urban environmental psychology. Security sense training set. In the future, when the conditions are mature, the brainwave feedback data in the virtual reality scene can be used to carry out the machine learning of psychological security, so as to improve the accuracy of the psychological security data.

scholarly journals Evaluation of Urban Environmental and Economic Coordination Based on Discrete Mathematical Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chenbin Dou ◽  
Lan Zheng ◽  
Wenjuan Wang ◽  
Mohammad Shabaz
Keyword(s):  
Mathematical Model ◽  
Evaluation System ◽  
Evaluation Method ◽  
Social Economy ◽  
Living Environment ◽  
Human Beings ◽  
Economic Activities ◽  
Urban Living ◽  
Evaluation Time ◽  
Discrete Mathematical Model

The urban ecological environment is the material basis and condition for human beings to engage in social and economic activities and the supporting system for the formation and sustainable development of cities. With the acceleration of urbanization and industrialization, urban living environments and economic development have become the focus of people’s attention. This leads to the necessity of studying how to improve the quality of the urban living environment and promote the harmonious coexistence of population, natural environment, and social economy. Traditional methods focus on multiple regression models to evaluate the urban environmental and economic harmony, but this method does not consider the weight of each index, resulting in poor accuracy of the evaluation results. This paper proposes a discrete mathematical model to design the evaluation index and evaluation system of urban environmental and economic coordination. It calculates the weight of each index; carrying capacity of the urban environment, the value of each environmental factor, and the comprehensive value of the environment is determined. The static evaluation and dynamic evaluation are used to evaluate the coordination of the urban environmental economy. The experimental results show that the designed evaluation method of urban environmental economic coordination has high accuracy and effectively improves the reliability and evaluation time.

Evaluation of maker space index system based on machine learning and intelligent interactive system

2020 ◽  
Vol 39 (4) ◽  
pp. 5941-5952
Author(s):  
Yang Chunhe
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Index System ◽  
Evaluation System ◽  
Learning System ◽  
Interactive System ◽  
Learning Technology ◽  
Integrated Learning System ◽  
Performance Evaluation System ◽  
Innovation Space

Machine learning technology is the core of artificial intelligence and the basis of computer intelligence. In recent years, machine learning technology has integrated and developed different learning methods, and the research of integrated learning system with more flexible and efficient form is also emerging. In this paper, the authors analyze the maker space index system based on machine learning and intelligent interactive system. As a comprehensive innovation and entrepreneurship platform, mass innovation space has the characteristics of both existing entrepreneurship service system and knowledge innovation driven. Through the index score calculation, the related evaluation system is constructed, the final score of social support system is 61.4.Multi-factor performance evaluation system based on machine learning and artificial intelligence,this paper reveals the development and change law of maker space, and provides theoretical basis for the future operation and decision-making of maker space.

scholarly journals Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

2021 ◽  
Author(s):  
Abdelfatteh Haidine ◽  
Fatima Zahra Salmam ◽  
Abdelhak Aqqal ◽  
Aziz Dahbi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Cost Effective ◽  
Mobile Internet ◽  
Network Function ◽  
New Radio ◽  
4G Lte

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.

scholarly journals Artificial Intelligence: A New Tool in Oncologist's Armamentarium

2021 ◽  
Author(s):  
Vineet Talwar ◽  
Kundan Singh Chufal ◽  
Srujana Joga
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Organs At Risk ◽  
Elderly Care ◽  
External Validation ◽  
Data Access ◽  
Human Beings ◽  
Area Of Interest ◽  
Segmentation Image

AbstractArtificial intelligence (AI) has become an essential tool in human life because of its pivotal role in communications, transportation, media, and social networking. Inspired by the complex neuronal network and its functions in human beings, AI, using computer-based algorithms and training, had been explored since the 1950s. To tackle the enormous amount of patients' clinical data, imaging, histopathological data, and the increasing pace of research on new treatments and clinical trials, and ever-changing guidelines for treatment with the advent of novel drugs and evidence, AI is the need of the hour. There are numerous publications and active work on AI's role in the field of oncology. In this review, we discuss the fundamental terminology of AI, its applications in oncology on the whole, and its limitations. There is an inter-relationship between AI, machine learning and, deep learning. The virtual branch of AI deals with machine learning. While the physical branch of AI deals with the delivery of different forms of treatment—surgery, targeted drug delivery, and elderly care. The applications of AI in oncology include cancer screening, diagnosis (clinical, imaging, and histopathological), radiation therapy (image acquisition, tumor and organs at risk segmentation, image registration, planning, and delivery), prediction of treatment outcomes and toxicities, prediction of cancer cell sensitivity to therapeutics and clinical decision-making. A specific area of interest is in the development of effective drug combinations tailored to every patient and tumor with the help of AI. Radiomics, the new kid on the block, deals with the planning and administration of radiotherapy. As with any new invention, AI has its fallacies. The limitations include lack of external validation and proof of generalizability, difficulty in data access for rare diseases, ethical and legal issues, no precise logic behind the prediction, and last but not the least, lack of education and expertise among medical professionals. A collaboration between departments of clinical oncology, bioinformatics, and data sciences can help overcome these problems in the near future.

Applications of Machine Learning in Cyber Security Domain

2020 ◽  
pp. 64-82 ◽  
Author(s):  
Sailesh Suryanarayan Iyer ◽  
Sridaran Rajagopal
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cyber Security ◽  
Traditional Society ◽  
Future Trends ◽  
Human Beings ◽  
Online Transactions ◽  
Network Intrusion ◽  
The World ◽  
Applications Of Machine Learning

Knowledge revolution is transforming the globe from traditional society to a technology-driven society. Online transactions have compounded, exposing the world to a new demon called cybercrime. Human beings are being replaced by devices and robots, leading to artificial intelligence. Robotics, image processing, machine vision, and machine learning are changing the lifestyle of citizens. Machine learning contains algorithms which are capable of learning from historical occurrences. This chapter discusses the concept of machine learning, cyber security, cybercrime, and applications of machine learning in cyber security domain. Malware detection and network intrusion are a few areas where machine learning and deep learning can be applied. The authors have also elaborated on the research advancements and challenges in machine learning related to cyber security. The last section of this chapter lists the future trends and directions in machine learning and cyber security.

A Short Introduction to the Ethics of Artificial Intelligence

2020 ◽  
pp. 1-42
Author(s):  
S. Matthew Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intelligent Systems ◽  
Ethical Issues ◽  
Black Box ◽  
Human Beings ◽  
Key Issues ◽  
Long Term Impact ◽  
Bad Data

This introduction outlines in section I.1 some of the key issues in the study of the ethics of artificial intelligence (AI) and proposes ways to take these discussions further. Section I.2 discusses key concepts in AI, machine learning, and deep learning. Section I.3 considers ethical issues that arise because current machine learning is data hungry; is vulnerable to bad data and bad algorithms; is a black box that has problems with interpretability, explainability, and trust; and lacks a moral sense. Section I.4 discusses ethical issues that arise because current machine learning systems may be working too well and human beings can be vulnerable in the presence of these intelligent systems. Section I.5 examines ethical issues arising out of the long-term impact of superintelligence such as how the values of a superintelligent AI can be aligned with human values. Section I.6 presents an overview of the essays in this volume.

Applied-Information Technology and Low-Carbon Industry in Research on the Developmental Path and Evaluation System of Hengyang Low-Carbon Community

2014 ◽  
Vol 1014 ◽  
pp. 311-315
Author(s):  
Yue Wu ◽  
Zi Xiong Dong
Keyword(s):  
Information Technology ◽  
Evaluation System ◽  
Human Life ◽  
Living Environment ◽  
Evaluation Index System ◽  
Ecological Crisis ◽  
Low Carbon ◽  
Human Beings ◽  
Carbon Consumption ◽  
Carbon Transport

Community is based on a certain region of the community of human life. In the face of increasingly serious ecological crisis, a benign living environment has became the urgent need for human beings. Under the guidance of sustainable development and ecological civilization concepts, as well as relying on the paths of applied-information technology,Low-carbon industry,low-carbon transport and low-carbon consumption, we can establish an evaluation index system from low-carbon buildings, low-carbon production, low-carbon transport, low-carbon consumption etc.by the Delphi method and analysis hierarchy process (AHP), so as to promote the development of Hengyang low-carbon community.

scholarly journals Artificial Intelligence Based Body Sensor Network Framework—Narrative Review: Proposing an End-to-End Framework using Wearable Sensors, Real-Time Location Systems and Artificial Intelligence/Machine Learning Algorithms for Data Collection, Data Mining and Knowledge Discovery in Sports and Healthcare

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ashwin A. Phatak ◽  
Franz-Georg Wieland ◽  
Kartik Vempala ◽  
Frederik Volkmar ◽  
Daniel Memmert
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Collection ◽  
Real Time ◽  
Sensor Network ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Body Sensor Network

AbstractWith the rising amount of data in the sports and health sectors, a plethora of applications using big data mining have become possible. Multiple frameworks have been proposed to mine, store, preprocess, and analyze physiological vitals data using artificial intelligence and machine learning algorithms. Comparatively, less research has been done to collect potentially high volume, high-quality ‘big data’ in an organized, time-synchronized, and holistic manner to solve similar problems in multiple fields. Although a large number of data collection devices exist in the form of sensors. They are either highly specialized, univariate and fragmented in nature or exist in a lab setting. The current study aims to propose artificial intelligence-based body sensor network framework (AIBSNF), a framework for strategic use of body sensor networks (BSN), which combines with real-time location system (RTLS) and wearable biosensors to collect multivariate, low noise, and high-fidelity data. This facilitates gathering of time-synchronized location and physiological vitals data, which allows artificial intelligence and machine learning (AI/ML)-based time series analysis. The study gives a brief overview of wearable sensor technology, RTLS, and provides use cases of AI/ML algorithms in the field of sensor fusion. The study also elaborates sample scenarios using a specific sensor network consisting of pressure sensors (insoles), accelerometers, gyroscopes, ECG, EMG, and RTLS position detectors for particular applications in the field of health care and sports. The AIBSNF may provide a solid blueprint for conducting research and development, forming a smooth end-to-end pipeline from data collection using BSN, RTLS and final stage analytics based on AI/ML algorithms.

scholarly journals Social aspects of Artificial Intelligence – selected issues

2021 ◽  
pp. 279-294
Author(s):  
Marcin Kowalczyk
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Free Will ◽  
Biometric Identification ◽  
Surveillance Systems ◽  
Web Browsers ◽  
Human Beings ◽  
Digital Era ◽  
Dating Apps ◽  
On Line

The paper presents findings regarding AI and Machine Learning and how “thinking machines” differ from human beings? In the next part the paper presents the issue of AI and Machine Learning’s impact on day-to-day activities in the following areas: 1. Microtargetting and psychometrics – with the examples from the business and politics; 2. Surveillance systems, biometric identification, COVID 19 tracing apps etc. – the issue of privacy in the digital era; 3. The question of choice optimization (AI-driven Web browsers and dating apps, chatbots and virtual assistants etc.); whether free will still exist in the AI supported on-line environment? The article is summed up with conclusions.

scholarly journals A Generic Model of the World and Intelligence

10.29007/s6vh ◽  
2019 ◽  
Author(s):  
Harris Wang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Object Oriented ◽  
Generic Model ◽  
Human Beings ◽  
Primary Methods ◽  
The World ◽  
Object Hierarchies

The resurgence of interest in Artificial Intelligence and advances in several fronts of AI, machine learning with neural network in particular, have made us think again about the nature of intelligence, and the existence of a generic model that may be able to capture what human beings have in their mind about the world to empower them to present all kinds of intelligent behaviors. In this paper, we present Constrained Object Hierarchies (COHs) as such a generic model of the world and intelligence. COHs extend the well-known object-oriented paradigm by adding identity constraints, trigger constraints, goal constraints, and some primary methods that can be used by capable beings to accomplish various intelligence, such as deduction, induction, analogy, recognition, construction, learning and many others.In the paper we will first argue the need for such a generic model of the world and intelligence, and then present the generic model in detail, including its important constructs, the primary methods capable beings can use, as well as how different intelligent behaviors can be implemented and achieved with this generic model.

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