Comprehensive review of conventional and state-of-the-art detection methods of Cryptosporidium

Deep learning is a computer-based modeling approach, which is made up of many processing layers that are used to understand the representation of data with several levels of abstraction. This review paper presents the state of the art in deep learning to highlight the major challenges and contributions in computer vision. This work mainly gives an overview of the current understanding of deep learning and their approaches in solving traditional artificial intelligence problems. These computational models enhanced its application in object detection, visual object recognition, speech recognition, face recognition, vision for driverless cars, virtual assistants, and many other fields such as genomics and drug discovery. Finally, this paper also showcases the current developments and challenges in training deep neural network.

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Structural Damage Diagnosis and Prediction Using Machine Learning and Deep Learning Models: Comprehensive Review of Advances

10.20944/preprints201912.0149.v1 ◽

2019 ◽

Author(s):

Amir Mosavi

Keyword(s):

Machine Learning ◽

Composite Structures ◽

Structural Damage ◽

Structural Integrity ◽

State Of The Art ◽

Future Trends ◽

Learning Models ◽

Damage Diagnosis ◽

Comprehensive Review ◽

Initiation And Propagation

The loss of integrity and adverse effect on mechanical properties can be concluded as attributing miro/macro-mechanics damage in structures, especially in composite structures. Damage as a progressive degradation of material continuity in engineering predictions for any aspects of initiation and propagation requires to be identified by a trustworthy mechanism to guarantee the safety of structures. Besides the materials design, structural integrity and health are usually prone to be monitored clearly. One of the most powerful methods for the detection of damage is machine learning (ML). This paper presents the state of the art of ML methods and their applications in structural damage and prediction. Popular ML methods are identified and the performance and future trends are discussed.

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Equipment Replacement Decision Making:Opportunities and Challenges

Journal of the Transportation Research Forum ◽

10.5399/osu/jtrf.52.3.4180 ◽

2013 ◽

Author(s):

Wei (David) Fan ◽

Mason D. Gemar ◽

Randy Machemehl

Keyword(s):

Dynamic Programming ◽

Stochastic Dynamic Programming ◽

State Of The Art ◽

Stochastic Dynamic ◽

Comprehensive Review ◽

Equipment Replacement ◽

The Right ◽

Art And State ◽

Practice Literature ◽

Solution Methodology

The primary function of equipment managers is to replace the right equipment at the right time and at the lowest overall cost. In this paper, the opportunities and challenges associated with equipment replacement optimization (ERO) are discussed in detail. First, a comprehensive review of the state-of-the art and state-of-the practice literature for the ERO problem is conducted. Second, a dynamic programming (DP) based optimization solution methodology is presented to solve the ERO problem. The Bellman’s formulation for the ERO deterministic (DDP) and stochastic dynamic programming (SDP) problems are discussed in detail. Finally, comprehensive ERO numerical results and implications are given.

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Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/692 ◽

2020 ◽

Cited By ~ 7

Author(s):

Zheng Wang ◽

Zhixiang Wang ◽

Yinqiang Zheng ◽

Yang Wu ◽

Wenjun Zeng ◽

...

Keyword(s):

Video Analysis ◽

State Of The Art ◽

Future Research ◽

Low Resolution ◽

Research Directions ◽

Comprehensive Review ◽

Practical Applications ◽

Future Research Directions

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories --- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are available at https://github.com/lightChaserX/Awesome-Hetero-reID

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A Comprehensive Review Toward the State-of-the-Art in Failure and Lifetime Predictions of Power Electronic Devices

IEEE Transactions on Power Electronics ◽

10.1109/tpel.2018.2860587 ◽

2019 ◽

Vol 34 (5) ◽

pp. 4729-4746 ◽

Cited By ~ 18

Author(s):

Abu Hanif ◽

Yuechuan Yu ◽

Douglas DeVoto ◽

Faisal Khan

Keyword(s):

State Of The Art ◽

Electronic Devices ◽

The State ◽

Power Electronic ◽

Comprehensive Review

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Eine bibliometrische Analyse/PPC and machine learning A bibliometric analysis

wt Werkstattstechnik online ◽

10.37544/1436-4980-2020-04-54 ◽

2020 ◽

Vol 110 (04) ◽

pp. 220-225

Author(s):

Matthias Schmidt ◽

Janine Tatjana Maier ◽

Mark Grothkopp

Keyword(s):

Machine Learning ◽

State Of The Art ◽

Dynamic Environment ◽

Production Planning And Control ◽

Manufacturing Companies ◽

Comprehensive Review ◽

Planning And Control ◽

Individual Task ◽

The Individual ◽

And Control

Produzierende Unternehmen stehen in einem dynamischen Umfeld vor der Herausforderung eine zunehmende Datenmenge effizienter zu verarbeiten. In diesem Zusammenhang werden häufig Ansätze des maschinellen Lernens (ML) diskutiert. Der Beitrag stellt eine umfassende Aufarbeitung des Stands der Forschung bezogen auf den Einsatz von ML-Ansätzen in der Produktionsplanung und -steuerung (PPS) bereit. Daraus lässt sich der Forschungsbedarf in den einzelnen Aufgabengebieten der PPS ableiten.   In a dynamic environment, manufacturing companies face the challenge of processing an increasing amount of data more efficiently. In this context, approaches of machine learning (ML) are often discussed. This paper provides a comprehensive review of the state of the art regarding the use of ML approaches in production planning and control (PPC). Based on this, the need for research in the individual task areas of PPC can be derived.

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