Domain Specific Publication Recommendation System using Deep Learning Approach

2019 ◽  
Author(s):  
Muhsina V P ◽  
Naseer C
Keyword(s):  
Deep Learning ◽  
Recommendation System ◽  
Learning Approach ◽  
Domain Specific ◽  
Publication Recommendation

scholarly journals Adaptable Recommendation System for Outfit Selection with Deep Learning Approach

2021 ◽  
Vol 54 (13) ◽  
pp. 605-610
Author(s):  
Laura J. Padilla Reyes ◽  
Natalia Bonifaz Oviedo ◽  
Edgar C. Camacho ◽  
Juan M. Calderon
Keyword(s):  
Deep Learning ◽  
Recommendation System ◽  
Learning Approach

scholarly journals Convolutional Neural Networks for Recognition of Lymphoblast Cell Images

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Tatdow Pansombut ◽  
Siripen Wikaisuksakul ◽  
Kittiya Khongkraphan ◽  
Aniruth Phon-on
Keyword(s):  
Deep Learning ◽  
Lymphoblastic Leukaemia ◽  
Specific Problem ◽  
Support Vector ◽  
Feature Engineering ◽  
Learning Approach ◽  
Domain Specific ◽  
Vector Machines ◽  
Multilayer Architecture

This paper presents the recognition for WHO classification of acute lymphoblastic leukaemia (ALL) subtypes. The two ALL subtypes considered are T-lymphoblastic leukaemia (pre-T) and B-lymphoblastic leukaemia (pre-B). They exhibit various characteristics which make it difficult to distinguish between subtypes from their mature cells, lymphocytes. In a common approach, handcrafted features must be well designed for this complex domain-specific problem. With deep learning approach, handcrafted feature engineering can be eliminated because a deep learning method can automate this task through the multilayer architecture of a convolutional neural network (CNN). In this work, we implement a CNN classifier to explore the feasibility of deep learning approach to identify lymphocytes and ALL subtypes, and this approach is benchmarked against a dominant approach of support vector machines (SVMs) applying handcrafted feature engineering. Additionally, two traditional machine learning classifiers, multilayer perceptron (MLP), and random forest are also applied for the comparison. The experiments show that our CNN classifier delivers better performance to identify normal lymphocytes and pre-B cells. This shows a great potential for image classification with no requirement of multiple preprocessing steps from feature engineering.

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions
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