Titlebook: Information and Communication Technologies; 12th Ecuadorian Conf Santiago Berrezueta-Guzman,Rommel Torres,Jorge Her Conference proceedings

Platelet

STRIA

Lorena Recalde,Gabriela Suntaxi,Diana Martinez-Mosquera,Rommel Masabanda,Danny Cabreraheir stakeholders’ interests — regardless of their product lines. Corporations that renege on their social responsibilities — and more so those without a compelling reason — tend to lose societal and stakeholder support or, as Davis noted more than three decades ago, their social power:.Corporations

适宜

GAVEL

GLEAN

follicular-unit

ear-canal

Marcos Orellana,Jonnathan Emmanuel Cubero Lupercio,Juan Fernando Lima,Patricio Santiago García-MonteIn common with all other professionals, our work has led to the experience of ethical dilemmas: typically, these have involved major confrontations, either with our col­ leagues or our consciences. This book, however, is not limited to a discussion of such major themes. Rather, we have tried to use

客观

Roberth Chachalo,Jaime Astudillo,Saba Infante,Israel Pinedaproblems. In common with all other professionals, our work has led to the experience of ethical dilemmas: typically, these have involved major confrontations, either with our col­ leagues or our consciences. This book, however, is not limited to a discussion of such major themes. Rather, we have tri

使腐烂

Image Classification of Peach Leaves Using a Siamese Neural Network vertical farming and aquaponics have been proposed to maximize output while conserving resources and space. However, there is still room for improvement in crop care processes. Deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a leading approach for addressing various

Heretical

Pancreas Segmentation Using SRGAN Combined with U-Net Neural Network vital for early diagnosis. This study focused on accurate pancreatic segmentation in medical images using subtle changes in 2D U-Net architecture to aid in diagnosis and treatment. Additionally, we proposed using a super-resolution generative adversarial network (SRGAN) for better-resolution images