Data-centric ai: Perspectives and challenges
The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …
A review of multisensor data fusion solutions in smart manufacturing: Systems and trends
Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0
revolution. Multiple sensors are used for industrial monitoring of machines and workers in …
revolution. Multiple sensors are used for industrial monitoring of machines and workers in …
Data-centric artificial intelligence: A survey
Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …
of its great success is the availability of abundant and high-quality data for building machine …
[HTML][HTML] Detection of polycystic ovarian syndrome using follicle recognition technique
B Rachana, T Priyanka, KN Sahana… - Global Transitions …, 2021 - Elsevier
Polycystic ovary syndrome is a disorder involving prolonged menstrual cycle, and often
excess androgen level normally occurs in several women at the time of their reproductive …
excess androgen level normally occurs in several women at the time of their reproductive …
Detection and classification of COVID-19 disease from X-ray images using convolutional neural networks and histogram of oriented gradients
COVID-19 is now regarded as the most lethal disease caused by the novel coronavirus
disease of humans. The COVID-19 pandemic has spread to every country on the planet and …
disease of humans. The COVID-19 pandemic has spread to every country on the planet and …
Switchtab: Switched autoencoders are effective tabular learners
Self-supervised representation learning methods have achieved significant success in
computer vision and natural language processing (NLP), where data samples exhibit explicit …
computer vision and natural language processing (NLP), where data samples exhibit explicit …
Towards effective classification of brain hemorrhagic and ischemic stroke using CNN
Brain stroke is one of the most leading causes of worldwide death and requires proper
medical treatment. Therefore, in this paper, our aim is to classify brain computed tomography …
medical treatment. Therefore, in this paper, our aim is to classify brain computed tomography …
CPD-CCNN: classification of pepper disease using a concatenation of convolutional neural network models
Agricultural products are vital to the sustainability of the economies of developing countries.
Most developing countries' economies such as Ethiopia heavily rely on agriculture. On a …
Most developing countries' economies such as Ethiopia heavily rely on agriculture. On a …
LCD-capsule network for the detection and classification of lung cancer on computed tomography images
Lung cancer is the second most prominent cancer in men and women, and it is also the
leading cause of cancer-related mortality. If lung cancer is diagnosed early, when it is …
leading cause of cancer-related mortality. If lung cancer is diagnosed early, when it is …
[HTML][HTML] Development of a chickpea disease detection and classification model using deep learning
AJ Belay, AO Salau, M Ashagrie, MB Haile - Informatics in medicine …, 2022 - Elsevier
Ethiopia is the largest producer of chickpeas in Africa. Crop production and yield in Ethiopia
is greatly affected by plant diseases which cause loss of agricultural products every year …
is greatly affected by plant diseases which cause loss of agricultural products every year …