scholarly journals OctopusNet: Machine learning for intelligent management of surgical tools

Smart Health ◽  
2021 ◽  
pp. 100244
Author(s):  
Mark Rodrigues ◽  
Michael Mayo ◽  
Panos Patros
Keyword(s):  
Machine Learning ◽  
Surgical Tools ◽  
Intelligent Management

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

scholarly journals ACTIVITY RECOGNITION FOR AMBIENT SENSING DATA AND RULE BASED ANOMALY DETECTION

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 379-382
Author(s):  
E. Seyedkazemi Ardebili ◽  
S. Eken ◽  
K. Küçük
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Smart Home ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Mathematical Methods ◽  
Abnormal State ◽  
Intelligent Management ◽  
Ambient Sensing ◽  
Multiple Residents

Abstract. After a brief look at the smart home, we conclude that to have a smart home, and it is necessary to have an intelligent management center. In this article, We have tried to make it possible for the smart home management center to be able to detect the presence of an abnormal state in the behavior of someone who lives in the house. In the proposed method, the daily algorithm examines the rate of changes of a person and provides a number which is henceforth called NNC (Number of normal changes) based on the person’s behavioral changes. We achieve the NNC number using a machine learning algorithm and performing a series of several simple statistical and mathematical calculations. NNC is a number that shows abnormal changes in residents’ behaviors in a smart home, i.e., this number is a small number for a regular person with constant planning and for a person who may not have any fixed principles and regular in personal life is a big number.To increase our accuracy in calculating NNC, we review all common machine learning algorithms and after tests we choose the decision tree because of its higher accuracy and speed and finally, NNC number is obtained by combining the Decision Tree algorithm with statistical and mathematical methods. In this method, we present a set of states and information obtained from the sensors along with the activities performed by the occupant of the house over a period of several days to the proposed algorithm. and the method ahead generates the main NNC number for those days for anyone living in a smart home. To generate this main NNC, we calculate each person’s daily NNC. That means we have daily NNCs for each person (based on his/her behaviors on that day) and the main NNC is the average of these daily NNC. We chose ARAS dataset (Human Activity Datasets in Multiple Homes with Multiple Residents) to implement our method and after tests and replications on the ARAS dataset, and to find anomalies in each person’s behavior in a day, we compare the main (average) NNC with that person’s daily NNC on that day. Finally, we can say, if the main NNC changes more than 30%, there is a possibility of an abnormality. and if the NNC changes more than 60% percent, we can say that an abnormal state or an uncommon event happened that day, and a declaration of an abnormal state will be issued to the resident of the house.

scholarly journals Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Saeed H. Alsamhi ◽  
Faris A. Almalki ◽  
Hatem Al-Dois ◽  
Soufiene Ben Othman ◽  
Jahan Hassan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Dynamic Environments ◽  
Iot Applications ◽  
Smart Healthcare ◽  
Intelligent Management ◽  
Reduce Energy Consumption ◽  
Iot Devices ◽  
Shed Light

The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

scholarly journals Towards Big Industrial Data Mining Through Explainable Automated Machine Learning

2021 ◽  
Author(s):  
Moncef Garouani ◽  
Adeel Ahmad ◽  
Mourad Bouneffa ◽  
Mohamed Hamlich ◽  
Gregory Bourguin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Software Platform ◽  
Management Tools ◽  
Industrial Data ◽  
Industrial Systems ◽  
Expert User ◽  
Automated Machine Learning ◽  
Intelligent Management

Abstract Industrial systems resources are capable of producing large amount of data. These data are often in heterogeneous formats and distributed, yet they provide means to mine the information which can allow the deployment of intelligent management tools for production activities. For this purpose, it is necessary to be able to implement knowledge extraction and prediction processes using Artificial Intelligence(AI) models but the selection and configuration of intended AI models tend to be increasingly complex for a non-expert user. In this paper, we present an approach and a software platform that may allow industrial actors, who are usually not familiar with AI, to select and configure algorithms optimally adapted to their needs. Hence, the approach is essentially based on automated machine learning. The resulting platform effectively enables a better choice among the combination of AI algorithms and hyper-parameter configurations. It also makes it possible to provide features of explainability of the resulting algorithms and models, thus increasing the acceptability of these models in practicing community of the users. The proposed approach has been applied in the field of predictive maintenance. Current tests are based on the analysis of more than 360 databases from the subjected field.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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