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Revolutionizing plant disease diagnosis: Pre-trained models outperform traditional methods

Revolutionizing plant disease diagnosis: Pre-trained models outperform traditional methods
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Xinyu Dong , Convolutional Neural Networks Cnns , Convolutional Neural Networks , Plant Phenomics ,

"A novel approach to enhance the quality of health care recommender sys" by Devendra Gautam, Anurag Dixit et al.

In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user's or patient's illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value ....

Health Care Recommender System , Convolutional Neural Networks , Very Light , Restricted Boltzman , Convolutional Neural Networks , Deep Learning , Uzzy Logics , Recommender System ,

"What Constitute an Effective Edge Detection Algorithm?" by Prashan Premaratne and Peter Vial

Edge detection is essential in every aspect of computer vision from vehicle number plate recognition to object detection in images or video. Preprocessing stage of many image processing tasks usually associated with Artificial Intelligence are relying on separating multiple objects from each other before accessing any other information. Over the years, researchers have used Sobel, Prewet or Roberts filter and then relying more on robust Canny method. Today, there have been many improvements on the earlier approaches, and now very much rely on Convolutional Neural Networks to assist in determining effective edges that would assist immensely in object detection. From that perspective, it is quite evident that effective edge detection is all about eventual object detection. With this notion in mind, it is easy to see what methods work and what methods would not achieve the goals. Deep Learning (DL) approaches have been gaining popularity over the years. Do DL algorithms outperform the con ....

Convolutional Neural Networks , Artificial Intelligence , Deep Learning , Edge Detection ,