CloudBridge Waste Segregator Automation using Machine Learning

There is a huge problem in creating space today because of growing population and research is going on profusely in finding space to dump waste. The waste has been dumped to rivers, underground and mixed with soil and by other methods. But all these methods are harmful to environment in long term. Our research is done on finding efficient way to segregate waste followed by recycling of wastes. The difficulties in isolation of various products are dealt using machine learning approach. The framework used to robotize the procedure of waste isolation to deal with the junk effectively and productively is one of the Machine Learning strategies called Convolutional Neural Network (CNN). The experiments showed that the performance of CNN is better because it recognizes the components in an image and recombines these components to recognize other structures while other methods learn to recognize as they go through it. The work will be segregated into 6 bins consisting of biodegradable, non- biodegradable. Here we have used the TensorFlow algorithm which uses Python. The applications of TensorFlow are Python application itself. The application of our research includes waste segregation in society, in industries, in agricultural fields. The recycled wastes can be used as organic material in many places

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Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

Monte Carlo Methods and Applications ◽

10.1515/mcma-2020-2080 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Idris Kharroubi ◽

Thomas Lim ◽

Xavier Warin

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Differential Equations ◽

Numerical Experiments ◽

Optimization Problem ◽

Learning Approach ◽

The Neural Network ◽

Machine Learning Approach ◽

Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

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New-Onset Diabetes and Preexisting Diabetes Are Associated With Comparable Reduction in Long-Term Survival After Liver Transplant: A Machine Learning Approach

Mayo Clinic Proceedings ◽

10.1016/j.mayocp.2018.06.020 ◽

2018 ◽

Vol 93 (12) ◽

pp. 1794-1802 ◽

Cited By ~ 9

Author(s):

Venkat Bhat ◽

Mahmood Tazari ◽

Kymberly D. Watt ◽

Mamatha Bhat

Keyword(s):

Machine Learning ◽

Liver Transplant ◽

Learning Approach ◽

Term Survival ◽

Long Term Survival ◽

Onset Diabetes ◽

Machine Learning Approach ◽

New Onset

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Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China

Journal of Advanced Transportation ◽

10.1155/2020/8847752 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Qingfeng Zhou ◽

Chun Janice Wong ◽

Xian Su

Keyword(s):

Machine Learning ◽

Sustainable Development ◽

Clustering Algorithms ◽

Utilization Efficiency ◽

Impact Factors ◽

Classification Algorithms ◽

Learning Approach ◽

High Efficient ◽

Machine Learning Approach

Since the number of bicycles is critical to the sustainable development of dockless PBS, this research practiced the introduction of a machine learning approach to quantity management using OFO bike operation data in Shenzhen. First, two clustering algorithms were used to identify the bicycle gathering area, and the available bike number and coefficient of available bike number variation were analyzed in each bicycle gathering area’s type. Second, five classification algorithms were compared in the accuracy of distinguishing the type of bicycle gathering areas using 25 impact factors. Finally, the application of the knowledge gained from the existing dockless bicycle operation data to guide the number planning and management of public bicycles was explored. We found the following. (1) There were 492 OFO bicycle gathering areas that can be divided into four types: high inefficient, normal inefficient, high efficient, and normal efficient. The high inefficient and normal inefficient areas gathered about 110,000 bicycles with low usage. (2) More types of bicycle gathering area will affect the accuracy of the classification algorithm. The random forest classification had the best performance in identifying bicycle gathering area types in five classification algorithms with an accuracy of more than 75%. (3) There were obvious differences in the characteristics of 25 impact factors in four types of bicycle gathering areas. It is feasible to use these factors to predict area type to optimize the number of available bicycles, reduce operating costs, and improve utilization efficiency. This work helps operators and government understand the characteristics of dockless PBS and contributes to promoting long-term sustainable development of the system through a machine learning approach.

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Generating Interactive Worlds with Text

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i02.5532 ◽

2020 ◽

Vol 34 (02) ◽

pp. 1693-1700 ◽

Cited By ~ 1

Author(s):

Angela Fan ◽

Jack Urbanek ◽

Pratik Ringshia ◽

Emily Dinan ◽

Emma Qian ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Common Sense ◽

Learning Approach ◽

Adventure Game ◽

Machine Learning Approach ◽

Game Environment ◽

Game Elements ◽

Construction Algorithms ◽

Game Environments

Procedurally generating cohesive and interesting game environments is challenging and time-consuming. In order for the relationships between the game elements to be natural, common-sense has to be encoded into arrangement of the elements. In this work, we investigate a machine learning approach for world creation using content from the multi-player text adventure game environment LIGHT (Urbanek et al. 2019). We introduce neural network based models to compositionally arrange locations, characters, and objects into a coherent whole. In addition to creating worlds based on existing elements, our models can generate new game content. Humans can also leverage our models to interactively aid in worldbuilding. We show that the game environments created with our approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.

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Machine Learning Approach to Analyse Ensemble Models and Neural Network Model for E-Commerce Application

Indian Journal of Science and Technology ◽

10.17485/ijst/v13i28.927 ◽

2020 ◽

Vol 13 (28) ◽

pp. 2849-2857

Author(s):

P Kalaivani ◽

Keyword(s):

Neural Network ◽

Machine Learning ◽

Network Model ◽

Neural Network Model ◽

Learning Approach ◽

Ensemble Models ◽

Machine Learning Approach

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A Machine‐Learning Approach to Derive Long‐Term Trends of Thermospheric Density

Geophysical Research Letters ◽

10.1029/2020gl087140 ◽

2020 ◽

Vol 47 (6) ◽

Author(s):

Libin Weng ◽

Jiuhou Lei ◽

Jiahao Zhong ◽

Xiankang Dou ◽

Hanxian Fang

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Machine Learning Approach ◽

Long Term Trends

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Machine Learning Approach to Predict International Roughness Index Using Long-Term Pavement Performance Data

Journal of Transportation Engineering Part B Pavements ◽

10.1061/jpeodx.0000312 ◽

2021 ◽

Vol 147 (4) ◽

pp. 04021058

Author(s):

Farshid Damirchilo ◽

Arash Hosseini ◽

Mahour Mellat Parast ◽

Elham H. Fini

Keyword(s):

Machine Learning ◽

Performance Data ◽

Pavement Performance ◽

Learning Approach ◽

International Roughness Index ◽

Machine Learning Approach ◽

Roughness Index

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Detection of Abnormal Activity to Alert the Nearby Persons via M-DNN Based Surveillance System

WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL ◽

10.37394/23203.2021.16.61 ◽

2021 ◽

Vol 16 ◽

pp. 668-685

Author(s):

Shankargoud Patil ◽

Kappargaon S. Prabhushetty

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Support Vector ◽

Learning Approach ◽

Normal Behavior ◽

Machine Learning Approach ◽

Artificial Neural Network Ann ◽

Static Conditions ◽

Advanced System

In today's environment, video surveillance is critical. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various wary activities from the live tracking of footages. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In a theoretical setting, a deep learning approach is utilized to detect suspicious or normal behavior and sends an alarm to the nearby people if suspicious activity is predicted. In this paper, data fusion technique is used for feature extraction which gives an accurate outcome. Moreover, the classes are classified by the well effective machine learning approach of modified deep neural network (M-DNN), that predicts the classes very well. The proposed method gains 95% accuracy, as well the advanced system is contrast with previous methods like artificial neural network (ANN), random forest (RF) and support vector machine (SVM). This approach is well fitted for dynamic and static conditions.

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