Literature Search
Users can search the literature by keywords. We offer three literature platforms for searching: IEEE, Arxiv and Paper with Code.
Function
import trendflow as tf
tf.search_papers('machine learning', 2018, 2022, 50, to_pandas=True)
Parameters
- query (string): keywords for searching literature
- start_year (int): start publication year of the papers
- end_year (int): end publication year of the papers
- num_papers (int): number of papers to search
- to_pandas (bool): if set to true, then the search results are converted to
pd.Dataframe, otherwise a list of dicts
Return
return a dict object containing 3 keys: 'ieee', 'arxiv' and 'paper_with_code'. The values for each key is a pd.Dataframe if to_pandas is True, otherwise a list of dicts.
example output
{'ieee': title ... conference_dates
0 IEEE Approved Draft Guide for Architectural Fr... ... NaN
1 IEEE Draft Guide for Architectural Framework a... ... NaN
2 Survey on lie group machine learning ... NaN
3 Machine learning-based distinction of left and... ... 1-5 Nov. 2021
4 Machine-Learning Prediction of Informatics Stu... ... 29-30 Jan. 2022
5 Imbalanced Data Classification Based on Extrem... ... 15-18 July 2018
6 IEEE Draft Standard for Technical Framework an... ... NaN
7 IEEE Draft Standard for Technical Framework an... ... NaN
8 A machine learning approach to predict the res... ... 25-27 Feb. 2022
9 Multimodal Representation Learning: Advances, ... ... 7-10 July 2019
10 Unsupervised Machine Learning Methods for Arti... ... 1-5 Nov. 2021
11 Predicting Creditworthiness of Smartphone User... ... 29-30 Jan. 2022
12 Classification of Mobile Phone Price Dataset U... ... 22-24 July 2022
13 Active Learning and Machine Teaching for Onlin... ... 13-16 Dec. 2021
14 Fleet learning of thermal error compensation i... ... 7-10 Sept. 2021
15 Machine Learning-Based Heart Disease Predictio... ... 22-25 Aug. 2022
16 Sentiment Analysis of Covid19 Vaccines Tweets ... ... 26-27 May 2022
17 Diagnosing Autism Spectrum Disorder Using Mach... ... 15-17 Sept. 2021
18 Comparison of Different Machine Learning Algor... ... 26-27 May 2022
19 Empirical Research on Multifactor Quantitative... ... 22-24 July 2022
20 Fashion Images Classification using Machine Le... ... 8-9 May 2022
21 Toward Machine Learning and Big Data Approache... ... 9-11 Dec. 2019
22 A novel method for detecting disk filtration a... ... NaN
23 Machine Learning Opportunities In Cloud Comput... ... 26-28 Nov. 2018
24 A Review on Machine Learning Styles in Compute... ... NaN
25 Traffic Prediction for Intelligent Transportat... ... 7-8 Feb. 2020
26 Cyberattacks Predictions Workflow using Machin... ... 16-17 Dec. 2021
27 Comparison Of Different Machine Learning Metho... ... 5-7 Aug. 2022
28 A Multi-source Based Healthcare Method for Hea... ... 14-14 Nov. 2021
29 Precise Medical Diagnosis For Brain Tumor Dete... ... 12-13 Nov. 2022
30 What are they Researching? Examining Industry-... ... 17-20 Dec. 2018
31 Fuzzt Set-Based Kernel Extreme Learning Machin... ... 4-5 Dec. 2021
32 An Overview of Machine Learning Techniques for... ... 8-9 Oct. 2022
33 IEEE Standard for Technical Framework and Requ... ... NaN
34 Crab Molting Identification using Machine Lear... ... 29-30 Jan. 2022
35 Virus Prediction Using Machine Learning Techni... ... 25-26 March 2022
36 Review on evaluation techniques for better stu... ... 28-30 April 2021
37 Classifying Quality of Web Services Using Mach... ... 28-29 Dec. 2021
38 Improve the Accuracy of Students Admission at ... ... 1-3 March 2022
39 Using Electronic Health Records and Machine Le... ... 3-7 Dec. 2018
40 Machine Learning for Efficient Assessment and ... ... 23-24 Oct. 2018
41 Machine learning-based recommendation trust mo... ... 6-8 Dec. 2018
42 Content-Based Recommendation Using Machine Lea... ... 25-28 Oct. 2021
43 Reliability Analysis and Optimization of Compu... ... 19-21 June 2022
44 Research on Radio Frequency Finerprint Licaliz... ... 3-5 Dec. 2021
45 Provide an Improved Model for Detecting Persia... ... 11-12 May 2022
46 Quantum Computing and Quantum Machine Learning... ... 8-9 Oct. 2022
47 Development of Machine-Learning Algorithms for... ... 1-5 Nov. 2021
48 Broken Rotor Bars Fault Detection in Induction... ... 6-10 May 2022
49 The Top 10 Risks of Machine Learning Security ... NaN
[50 rows x 34 columns], 'arxiv': id ... arxiv:journal_ref
0 http://arxiv.org/abs/1909.03550v1 ... NaN
1 http://arxiv.org/abs/1811.04422v1 ... NaN
2 http://arxiv.org/abs/1707.04849v1 ... NaN
3 http://arxiv.org/abs/1909.09246v1 ... NaN
4 http://arxiv.org/abs/2301.09753v1 ... NaN
5 http://arxiv.org/abs/0904.3664v1 ... NaN
6 http://arxiv.org/abs/2012.04105v1 ... NaN
7 http://arxiv.org/abs/2204.07492v2 ... {'@xmlns:arxiv': 'http://arxiv.org/schemas/ato...
8 http://arxiv.org/abs/1911.06612v1 ... NaN
9 http://arxiv.org/abs/1909.01866v1 ... {'@xmlns:arxiv': 'http://arxiv.org/schemas/ato...
10 http://arxiv.org/abs/1903.08801v1 ... NaN
11 http://arxiv.org/abs/1907.08908v1 ... NaN
12 http://arxiv.org/abs/1707.09562v3 ... NaN
13 http://arxiv.org/abs/2108.07915v1 ... NaN
14 http://arxiv.org/abs/2206.07090v1 ... NaN
15 http://arxiv.org/abs/1507.02188v1 ... NaN
16 http://arxiv.org/abs/1212.2686v1 ... NaN
17 http://arxiv.org/abs/2001.04942v2 ... NaN
18 http://arxiv.org/abs/1607.02450v2 ... NaN
19 http://arxiv.org/abs/2007.01503v1 ... NaN
20 http://arxiv.org/abs/1906.06821v2 ... NaN
21 http://arxiv.org/abs/1911.00776v1 ... NaN
22 http://arxiv.org/abs/2201.01288v1 ... NaN
23 http://arxiv.org/abs/2011.11819v1 ... NaN
24 http://arxiv.org/abs/2004.00993v2 ... NaN
25 http://arxiv.org/abs/2009.11087v1 ... NaN
26 http://arxiv.org/abs/2003.05155v2 ... NaN
27 http://arxiv.org/abs/1706.08001v1 ... NaN
28 http://arxiv.org/abs/1207.4676v2 ... NaN
29 http://arxiv.org/abs/1603.02185v1 ... NaN
30 http://arxiv.org/abs/1910.12387v2 ... NaN
31 http://arxiv.org/abs/2007.05479v1 ... NaN
32 http://arxiv.org/abs/2007.14206v1 ... NaN
33 http://arxiv.org/abs/1908.04710v3 ... {'@xmlns:arxiv': 'http://arxiv.org/schemas/ato...
34 http://arxiv.org/abs/2002.12364v1 ... {'@xmlns:arxiv': 'http://arxiv.org/schemas/ato...
35 http://arxiv.org/abs/2001.09608v1 ... NaN
36 http://arxiv.org/abs/1509.00913v3 ... NaN
37 http://arxiv.org/abs/2110.12773v1 ... NaN
38 http://arxiv.org/abs/1607.01400v1 ... {'@xmlns:arxiv': 'http://arxiv.org/schemas/ato...
39 http://arxiv.org/abs/2202.10564v1 ... NaN
40 http://arxiv.org/abs/1510.00633v1 ... NaN
41 http://arxiv.org/abs/1802.03830v1 ... NaN
42 http://arxiv.org/abs/2106.07032v1 ... NaN
43 http://arxiv.org/abs/1612.04858v1 ... NaN
44 http://arxiv.org/abs/1702.08608v2 ... NaN
45 http://arxiv.org/abs/1705.07538v2 ... NaN
46 http://arxiv.org/abs/1808.00033v3 ... NaN
47 http://arxiv.org/abs/1911.08587v1 ... NaN
48 http://arxiv.org/abs/2007.01977v1 ... NaN
49 http://arxiv.org/abs/2007.07981v1 ... NaN
[50 rows x 12 columns], 'paper_with_code': id ... proceeding
0 snap-ml-a-hierarchical-framework-for-machine ... neurips-2018-12
1 a-novel-hybrid-machine-learning-model-for ... None
2 orthogonal-machine-learning-power-and ... icml-2018-7
3 on-machine-learning-and-structure-for-mobile ... None
4 data-driven-decentralized-optimal-power-flow ... None
5 interpretable-machine-learning-for-privacy ... None
6 a-machine-learning-item-recommendation-system ... None
7 plug-in-regularized-estimation-of-high ... None
8 static-malware-detection-subterfuge ... None
9 two-use-cases-of-machine-learning-for-sdn ... None
10 ml-fv-heartsuit-a-survey-on-the-application ... None
11 can-machine-learning-identify-interesting ... None
12 a-hybrid-econometric-machine-learning ... None
13 machine-learning-cicy-threefolds ... None
14 machine-learning-based-colon-deformation ... None
15 residual-unfairness-in-fair-machine-learning ... icml-2018-7
16 online-adaptive-machine-learning-based ... None
17 reduced-order-modeling-through-machine ... None
18 opportunities-in-machine-learning-for ... None
19 a-machine-learning-framework-for-stock ... None
20 machine-learning-for-yield-curve-feature ... None
21 scikit-learn-machine-learning-in-python ... None
22 analysis-of-dawnbench-a-time-to-accuracy ... None
23 bindsnet-a-machine-learning-oriented-spiking ... None
24 learning-a-code-machine-learning-for ... None
25 ml-leaks-model-and-data-independent ... None
26 explaining-explanations-an-overview-of ... None
27 learning-from-exemplars-and-prototypes-in ... None
28 a-comparison-of-machine-learning-algorithms ... None
29 deploying-customized-data-representation-and ... None
30 a-guide-to-constraining-effective-field ... None
31 constraining-effective-field-theories-with ... None
32 interpreting-deep-learning-the-machine ... None
33 defending-against-machine-learning-model ... None
34 grader-variability-and-the-importance-of ... None
35 predictive-performance-modeling-for ... None
36 a-progressive-batching-l-bfgs-method-for ... icml-2018-7
37 currency-exchange-prediction-using-machine ... None
38 towards-computational-fluorescence-microscopy ... None
39 machine-learning-for-prediction-of-extreme ... None
40 on-formalizing-fairness-in-prediction-with ... None
41 intensive-preprocessing-of-kdd-cup-99-for ... None
42 model-based-pricing-for-machine-learning-in-a ... None
43 qunatification-of-metabolites-in-mr ... None
44 corpus-conversion-service-a-machine-learning-1 ... None
45 machine-learning-inference-of-fluid-variables ... None
46 wikipedia-for-smart-machines-and-double-deep ... None
47 the-marginal-value-of-adaptive-gradient ... neurips-2017-12
48 geomstats-a-python-package-for-riemannian ... iclr-2019-5
49 the-roles-of-supervised-machine-learning-in ... None
[50 rows x 13 columns]}