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	21	Intelligent Identification of Jute Pests Based on Transfer Learning and Deep Convolutional Neural Networks Enthalten in Neural processing letters 14.8.2022: 1-18
	22	A framework to enhance generalization of deep metric learning methods using general discriminative feature learning and class adversarial neural networks Enthalten in Applied intelligence 2.8.2022: 1-19
	23	Prediction carbonization yields and the sensitivity analyses using deep learning neural networks and support vector machines Enthalten in International journal of environmental science and technology 30.7.2022: 1-10
	24	Retraction Note to: Brain tumour classification using saliency driven nonlinear diffusion and deep learning with convolutional neural networks (CNN) Enthalten in Journal of ambient intelligence and humanized computing 20.6.2022: 1
	25	Detecting and responding to hostile disinformation activities on social media using machine learning and deep neural networks Enthalten in Neural computing & applications 9.6.2022: 1-23
	26	COLAM: Co-Learning of Deep Neural Networks and Soft Labels via Alternating Minimization Enthalten in Neural processing letters 13.5.2022: 1-15
	27	Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery Enthalten in International journal of energy and environmental engineering 19.4.2022: 1-16
	28	Pre-trained models are not enough: active and lifelong learning is important for long-term visual monitoring of mammals in biodiversity research—Individual identification and attribute prediction with image features from deep neural networks and decoupled decision models applied to elephants and great apes Enthalten in Mammalian biology 12.4.2022: 1-23
	29	Constitutive artificial neural networks Linka, Kevin. - Hamburg : Universitätsbibliothek der Technischen Universität Hamburg-Harburg, 2021
	30	MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning Müller, Dominik. - Augsburg : Universität Augsburg, 2021

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