Logistics engineering optimization based on machine learning and artificial intelligence technology

Important Means ◽

Practical Effect ◽

Combined Classifier ◽

In the era of the Internet of Things, smart logistics has become an important means to improve people’s life rhythm and quality of life. At present, some problems in logistics engineering have caused logistics efficiency to fail to meet people’s expected goals. Based on this, this paper proposes a logistics engineering optimization system based on machine learning and artificial intelligence technology. Moreover, based on the classifier chain and the combined classifier chain, this paper proposes an improved multi-label chain learning method for high-dimensional data. In addition, this study combines the actual needs of logistics transportation and the constraints of the logistics transportation process to use multi-objective optimization to optimize logistics engineering and output the optimal solution through an artificial intelligence model. In order to verify the effectiveness of the model, the performance of the method proposed in this paper is verified by designing a control experiment. The research results show that the logistics engineering optimization based on machine learning and artificial intelligence technology proposed in this paper has a certain practical effect.

An Agent-Based Approach to Realize Emergent Configurations in the Internet of Things

Electronics ◽

10.3390/electronics9091347 ◽

2020 ◽

Vol 9 (9) ◽

pp. 1347 ◽

Cited By ~ 1

Author(s):

Fahed Alkhabbas ◽

Romina Spalazzese ◽

Paul Davidsson

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Internet Of Things ◽

The Internet ◽

Daily Lives ◽

Agent Based ◽

Design Time ◽

Underlying Processes ◽

The Internet of Things (IoT) has enabled physical objects and devices, often referred to as things, to connect and communicate. This has opened up for the development of novel types of services that improve the quality of our daily lives. The dynamicity and uncertainty of IoT environments, including the mobility of users and devices, make it hard to foresee at design time available things and services. Further, users should be able to achieve their goals seamlessly in arbitrary environments. To address these challenges, we exploit Artificial Intelligence (AI) to engineer smart IoT systems that can achieve user goals and cope with the dynamicity and uncertainty of their environments. More specifically, the main contribution of this paper is an approach that leverages the notion of Belief-Desire-Intention agents and Machine Learning (ML) techniques to realize Emergent Configurations (ECs) in the IoT. An EC is an IoT system composed of a dynamic set of things that connect and cooperate temporarily to achieve a user goal. The approach enables the distributed formation, enactment, adaptation of ECs, and conflict resolution among them. We present a conceptual model of the entities of the approach, its underlying processes, and the guidelines for using it. Moreover, we report about the simulations conducted to validate the feasibility of the approach and evaluate its scalability.

Development of migrant workers in construction based on machine learning and artificial intelligence technology

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189499 ◽

2020 ◽

pp. 1-12

Author(s):

Chen Guang

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Labor Force ◽

Migrant Workers ◽

Engineering Industry ◽

Agricultural Industry ◽

Construction Engineering ◽

Chinese Labor ◽

The Times

Artificial intelligence technology has been widely used in all aspects of our life. Similarly, the application of artificial intelligence in the field of construction engineering is a necessary trend in the development of engineering industry, especially in the traditional construction engineering department. Under the background of the times, from the perspective of knowledge, artificial intelligence technology has appeared a huge development, which may have an impact on the employment of Chinese labor force, may create new jobs, or replace traditional jobs. This effect on employment is essential. From the perspective of machine learning and artificial intelligence, this paper reviews the transformation prospects of engineering industry and the development of agricultural industry in construction industry, and examines the intellectual transformation of individual human capital in Chinese labor force.

Application of Sensors in Intelligent Clothing Design

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v14i06.8694 ◽

2018 ◽

Vol 14 (06) ◽

pp. 4

Author(s):

Shali Jiang ◽

Qiong Ren

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Experimental Results ◽

Machine Learning Method ◽

Learning Method ◽

Clothing Design ◽

Contact Sensor ◽

Intelligent Clothing ◽

Contact Sensors

In order to study the application of sensors in intelligent clothing design, the artificially intelligent cutting-edge technology -machine learning method was proposed to combine a variety of signals of non-contact sensors in several different positions. Higher accuracy was achieved, while maintaining the comfort brought by a non-contact sensor. The experimental results showed that the proposed strategy focused on the combination of clothing design technology and artificial intelligence technology. As a result, without changing the sensor materials, it enhances the comfort and precision of clothing, eliminates the comfort reduced by sensor close to the skin, and transforms inaccurate measurement into accurate measurement.

IMPROVING INTERNET OF THINGS PARKING SYSTEMS

Facta Universitatis Series Automatic Control and Robotics ◽

10.22190/fuacr2003163b ◽

2021 ◽

Vol 19 (3) ◽

pp. 163

Author(s):

Dušan Bogićević

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Cloud Computing ◽

Internet Of Things ◽

Information Needs ◽

Vehicle Classification ◽

The Internet ◽

Parking Space ◽

Automated Systems ◽

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

Artificial Intelligence Technology

Machine Learning Based Behavioral Modification

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1299.0886s219 ◽

2019 ◽

Vol 8 (6S2) ◽

pp. 1134-1138

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Behavior Analysis ◽

Time Of Day ◽

Computational Techniques ◽

Behavioral Modification ◽

Optimal Temperature ◽

The Past ◽

Different Types ◽

To build up a particular profile about a person, the study of examining the comportment is known as Behavior analysis. Initially the Behavior analysis is used in psychology and for suggesting and developing different types the application content for user then it developed in information technology. To make the applications for user's personal needs it becoming a new trends with the use of artificial intelligence (AI). in many applications like innovation to do everything from anticipating buy practices to altering a home's indoor regulator to the inhabitant's optimal temperature for a specific time of day use machine learning and artificial intelligence technology. The technique that is use to advance the rule proficiency that rely upon the past experience is known as machine learning. By utilizing the insights hypothesis it makes the numerical model, and its real work is to infer from the models gave. To take the information clearly from the data the methodology utilizes computational techniques.

Improving Real-Time Drilling Data Quality Using Artificial Intelligence and Machine Learning Techniques

10.2118/204658-ms ◽

2021 ◽

Author(s):

S. H. Al Gharbi ◽

A. A. Al-Majed ◽

A. Abdulraheem ◽

S. Patil ◽

S. M. Elkatatny

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Data Quality ◽

Real Time ◽

Input Data ◽

Support Vector ◽

The Real ◽

Drilling Data ◽

Drilling Operations

Abstract Due to high demand for energy, oil and gas companies started to drill wells in remote areas and unconventional environments. This raised the complexity of drilling operations, which were already challenging and complex. To adapt, drilling companies expanded their use of the real-time operation center (RTOC) concept, in which real-time drilling data are transmitted from remote sites to companies’ headquarters. In RTOC, groups of subject matter experts monitor the drilling live and provide real-time advice to improve operations. With the increase of drilling operations, processing the volume of generated data is beyond a human's capability, limiting the RTOC impact on certain components of drilling operations. To overcome this limitation, artificial intelligence and machine learning (AI/ML) technologies were introduced to monitor and analyze the real-time drilling data, discover hidden patterns, and provide fast decision-support responses. AI/ML technologies are data-driven technologies, and their quality relies on the quality of the input data: if the quality of the input data is good, the generated output will be good; if not, the generated output will be bad. Unfortunately, due to the harsh environments of drilling sites and the transmission setups, not all of the drilling data is good, which negatively affects the AI/ML results. The objective of this paper is to utilize AI/ML technologies to improve the quality of real-time drilling data. The paper fed a large real-time drilling dataset, consisting of over 150,000 raw data points, into Artificial Neural Network (ANN), Support Vector Machine (SVM) and Decision Tree (DT) models. The models were trained on the valid and not-valid datapoints. The confusion matrix was used to evaluate the different AI/ML models including different internal architectures. Despite the slowness of ANN, it achieved the best result with an accuracy of 78%, compared to 73% and 41% for DT and SVM, respectively. The paper concludes by presenting a process for using AI technology to improve real-time drilling data quality. To the author's knowledge based on literature in the public domain, this paper is one of the first to compare the use of multiple AI/ML techniques for quality improvement of real-time drilling data. The paper provides a guide for improving the quality of real-time drilling data.

Recent Advances in Medical Image Processing

Acta Cytologica ◽

10.1159/000510992 ◽

2020 ◽

pp. 1-14

Author(s):

Zhen Huang ◽

Qiang Li ◽

Ju Lu ◽

Junlin Feng ◽

Jiajia Hu ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Image Processing ◽

Medical Imaging ◽

Medical Image ◽

Medical Image Processing ◽

Convolution Neural Network ◽

Deep Convolution Neural Network

Background: Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. Key Message: In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. Summary: This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

Management of Software-Defined Networking Powered by Artificial Intelligence

10.5772/intechopen.97197 ◽

2021 ◽

Author(s):

Jehad Ali ◽

Byeong-hee Roh

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Internet Of Things ◽

Heterogeneous Networks ◽

Software Defined Networking ◽

Machine Learning Algorithms ◽

The Internet ◽

Underlying Network ◽

And Control ◽

Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

THE FINANCIAL ASPECT OF IoT PROCESS AUTOMATION IN CREATING NEW VALUE FOR THE ECONOMY

EMC Review - Časopis za ekonomiju - APEIRON ◽

10.7251/emc1902384s ◽

2020 ◽

Vol 18 (2) ◽

Author(s):

Nedeljko Šikanjić ◽

Zoran Ž. Avramović ◽

Esad Jakupović

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Scientific Paper ◽

General Purpose ◽

Advanced Technology ◽

Process Automation ◽

Advanced Technologies ◽

Financial Benefits ◽

Financial Aspect

In today’s world, devices with possibility to communicate, are emerging and growing daily. This advanced technology is bringing ideas of how to use these devices, in order to gain financial benefits for enterprises, business and economy in general. Purpose of research in this scientific paper is to discover, what are the trends in connecting these devices, called internet of things (IoT), what are financial aspects of implementing IoT solutions and how leaders in area of cloud computing and IoT, are implementing additional advanced technologies such as machine learning and artificial intelligence, to improve processes and gain increase in revenue, while bringing automation in place for the end users. Development of informational society is not only bringing innovation to everyday life, but is also providing effect on the economy. This effect reflects on various business platforms, companies and organizations while increasing the quality of the end product or service that is being provided.

Artificial Intelligence Applications in the New Model of Logistics Development Based on Wireless Communication Technology

Scientific Programming ◽

10.1155/2021/5166993 ◽

2021 ◽

Vol 2021 ◽

pp. 1-5

Author(s):

Shuaiqi Wang

Keyword(s):

Artificial Intelligence ◽

Supply Chain ◽

Internet Of Things ◽

Cost Reduction ◽

Sharing Economy ◽

Smart Logistics ◽

Supply Chain Logistics ◽

Service Efficiency ◽

With the continuous development of artificial intelligence technology, the supply chain logistics industry has shown new changes. The products of the intelligent era such as smart devices, big data computing, and Internet of Things technology have gradually become the transformation and innovation of logistics and supply chain enterprises. The important driving force of the logistics and supply chain industry will greatly improve the operational efficiency of the logistics and supply chain industry. Methods. This article studies the technical framework of artificial intelligence and explores the upgrading and transformation of supply chain logistics enterprises in logistics infrastructure, production tools, and logistics operation processes under the promotion of artificial intelligence technology, from warehouse location, inventory management, warehousing operations, transportation, and distribution. The data analysis and prediction analyze the impact of artificial intelligence on the supply chain logistics field and finally point out the problems in the intelligent development of the supply chain logistics field and put forward targeted suggestions to promote the modern supply chain logistics to become more intelligent. developThe new development trend of smart logistics is towards sharing economy, automation, service efficiency, and cost reduction. Results. In this context, if logistics companies want to achieve higher-quality development, they cannot do without business model innovation and larger-scale collaboration, transparency of logistics information, and more comprehensive information sharing. The new trend of the development of smart logistics is to develop in the direction of sharing economy, automation, service efficiency, and cost reduction. Conclusion. Intelligence and the Internet of Things are the inevitable trend of the development of smart logistics, which is mainly realized through the Internet of Things path in terms of visual information technology, intelligent robot operation, vehicle scheduling, and cargo traceability.