Results 11 to 20 of about 427,913 (167)

EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides

Frontiers in Genetics, 2020
As cancer remains one of the main threats of human life, developing efficient cancer treatments is urgent. Anticancer peptides, which could overcome the significant side effects and poor results of traditional cancer treatments, have become a new ...
Ruiquan Ge, Guanwen Feng, Xiaoyang Jing, Renfeng Zhang, Pu Wang, Qing Wu   +5 more
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Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework

Frontiers in Plant Science, 2022
Plant resistance proteins (R proteins) recognize effector proteins secreted by pathogenic microorganisms and trigger an immune response against pathogenic microbial infestation.
Yifan Chen, Zejun Li, Zhiyong Li
doaj   +1 more source

Sentiment Analysis of Tweets Using Supervised Machine Learning Techniques Based on Term Frequency [PDF]

Journal of Information Technology Management, 2021
World of technology provides everyone with a great outlet to give their opinion, using social media like Twitter and other platforms. This paper employs machine learning methods for text analysis to obtain sentiments of reviews by the people on twitter ...
Deepti Aggarwal, Vikram Bali, Abhishek Agarwal, Kshitiz Poswal, Madhav Gupta, Abhishek Gupta   +5 more
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RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System

IEEE Access, 2022
Due to the rapid growth in network traffic and increasing security threats, Intrusion Detection Systems (IDS) have become increasingly critical in the field of cyber security for providing secure communications against cyber adversaries.
Zihan Wu, Hong Zhang, Penghai Wang, Zhibo Sun   +3 more
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Neural representation of feature synergy

NeuroImage, 2011
Interactive non-linear cooperation of different feature dimensions, feature synergy, has been studied in psychophysics, but the neural mechanism is unknown. The present study investigated the neural representation of feature synergy of two second-order visual features by combining electroencephalography (EEG) with the signal detection theory (SDT). Two
Tetsuo, Kida, Emi, Tanaka, Yasuyuki, Takeshima, Ryusuke, Kakigi   +3 more
openaire   +2 more sources

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

Remote Sensing, 2016
Automatic post-disaster mapping of building damage using remote sensing images is an important and time-critical element of disaster management. The characteristics of remote sensing images available immediately after the disaster are not certain, since ...
Anand Vetrivel, Markus Gerke, Norman Kerle, George Vosselman   +3 more
doaj   +1 more source

IEEE Access Special Section Editorial: Feature Representation and Learning Methods With Applications in Large-Scale Biological Sequence Analysis

IEEE Access, 2021
Machine learning has been widely applied in the fields of biomedicine, computational biology, bioinformatics, image processing, and so on. The performance of machine learning methods mainly relies on feature representation that is the mapping from ...
Feifei Cui, Quan Zou, Qin Ma, Leyi Wei, Jijun Tang, Dariusz Mrozek   +5 more
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CONCEPT REPRESENTATION WITH OVERLAPPING FEATURE INTERVALS [PDF]

Cybernetics and Systems, 1998
This article presents a new form of exemplar-based learning method, based on overlapping feature intervals. In this model, a concept is represented by a collection of overlappling intervals for each feature and class. Classification with Overlapping Feature Intervals COFI is a particular implementation of this technique. In this incremental, inductive,
Altay Güvenlr H., Koç H.G.
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Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder

Frontiers in Genetics, 2019
Identifying genes associated with Parkinson's disease plays an extremely important role in the diagnosis and treatment of Parkinson's disease. In recent years, based on the guilt-by-association hypothesis, many methods have been proposed to predict ...
Jiajie Peng, Jiaojiao Guan, Xuequn Shang
doaj   +1 more source

Neuroevolutionary Feature Representations for Causal Inference

, 2022
Within the field of causal inference, we consider the problem of estimating heterogeneous treatment effects from data. We propose and validate a novel approach for learning feature representations to aid the estimation of the conditional average treatment effect or cate. Our method focuses on an intermediate layer in a neural network trained to predict
Michael C. Burkhart, Gabriel Ruiz
openaire   +2 more sources