A comprehensive review on breast cancer detection, classification and segmentation using deep learning
The incidence and mortality rate of Breast Cancer (BC) are global problems for women, with
over 2.1 million new diagnoses each year worldwide. There is no age range, race, or …
over 2.1 million new diagnoses each year worldwide. There is no age range, race, or …
A survey on graph counterfactual explanations: definitions, methods, evaluation, and research challenges
Graph Neural Networks (GNNs) perform well in community detection and molecule
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …
Challenges of deep learning in medical image analysis—improving explainability and trust
Deep learning has revolutionized the detection of diseases and is helping the healthcare
sector break barriers in terms of accuracy and robustness to achieve efficient and robust …
sector break barriers in terms of accuracy and robustness to achieve efficient and robust …
A hierarchical fused fuzzy deep neural network with heterogeneous network embedding for recommendation
The integration of deep learning (DL) and fuzzy learning (FL) is considered a recently
emerging and promising research direction in data embedding. The integrated fuzzy neural …
emerging and promising research direction in data embedding. The integrated fuzzy neural …
X-ray image based COVID-19 detection using evolutionary deep learning approach
SMJ Jalali, M Ahmadian, S Ahmadian… - Expert Systems with …, 2022 - Elsevier
Radiological methodologies, such as chest x-rays and CT, are widely employed to help
diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns …
diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns …
RDERL: Reliable deep ensemble reinforcement learning-based recommender system
Recommender systems (RSs) have been employed for many real-world applications
including search engines, social networks, and information retrieval systems as powerful …
including search engines, social networks, and information retrieval systems as powerful …
Ubar: User behavior-aware recommendation with knowledge graph
The recommendation system is widely used in many aspects of digital economy to offer
personalized services, in which efficient capture of user–item relations is of critical …
personalized services, in which efficient capture of user–item relations is of critical …
BAR: Behavior-aware recommendation for sequential heterogeneous one-class collaborative filtering
In our daily life, we are often greatly assisted with recommendation engines in finding the
required information efficiently and accurately. In this paper, we focus on an emerging and …
required information efficiently and accurately. In this paper, we focus on an emerging and …
[HTML][HTML] Knowledge graph-based recommendation system enhanced by neural collaborative filtering and knowledge graph embedding
Recommendation systems are an important and undeniable part of modern systems and
applications. Recommending items and users to the users that are likely to buy or interact …
applications. Recommending items and users to the users that are likely to buy or interact …
Solar irradiance forecasting using a novel hybrid deep ensemble reinforcement learning algorithm
Solar irradiance forecasting is a major priority for the power transmission systems in order to
generate and incorporate the performance of massive photovoltaic plants efficiently. As …
generate and incorporate the performance of massive photovoltaic plants efficiently. As …