Telecommunications System & Management

In 2020, COVID-19 exposed the fragility of health sector. In the US in particular, the most expensive healthcare system in the world, it also faces a tremendous challenge in responding to diagnostic needs. One of the biggest challenges in medical imaging like MRI is not the high cost per se, but the capacity. An MRI session lasts between 15 and 60 minutes. There are hospitals with only one device or even no one. Medical imaging is one of the best use cases for AI in healthcare, but lack of physician engagement and data bottlenecks can make the technology less useful than promised. When used to decode the complicated nature of MRIs, CT scans, and other testing modalities, advanced analytical tools have proven their ability to extract meaningful information to improve decision-making, sometimes with greater precision than humans themselves. . With deep learning, it is possible to capture less data and thus scan faster, while preserving or even enhancing the rich information content of MRI images. The key is to train artificial neural networks to recognize the underlying structure of the images in order to fill in the missing views from the accelerated scan. This approach is similar to how humans process sensory information. When we experience the world, our brains often receive an incomplete image, as in the case of darkened or dimly lit objects, that we need to convert into actionable information.

Since the dawn of agriculture, crop monitoring and inspection remains a mainstay of every farmer’s routine. Today, many farmers visually inspect their crops armed with a variety of tools to help ensure ideal plant health and performance. Although human visual inspection remains an essential part of agriculture, it has many challenges and many limitations. Research over the last decade or so has assessed the applicability of computer vision and deep learning to address the crop inspection challenge [Nusk2011, Nusk2014, Blom2009, Herr2015]. These approaches have shown tremendous promise, they are only just now beginning to go beyond the research phase into commercialization. Around 2015, image processing methods using deep neural networks began to replace the earlier classical computer vision approach, providing both better performance and more generalizable results. Again, through the early work of the CMU team, a StalkNet [Bawe2018] architecture was developed, which combines an RCNN feature detector with a GAN based pixel segmenter. To date, StalkNet has been trained to measure dozens of widely varying features in different crops, ranging from leaf necrosis to fruit ripeness to sorghum seed size for grain yield. The first market Bloomfield has chosen to address is grape growing and vineyard inspection, but we see CEA as a natural next step in the progression of our technology and a large opportunity. Flash combines highresolution flash lighted stereo RGB images with a cloud-based deep learning pipeline to inspect and assess the health and performance of each and every plant in a field or grow, one plant at a time. The result, so far, with Bloomfield’s vineyard customers is yield estimation, pest/disease detection, labor saving and digitalization. This comprehensive analysis forms the foundation for Bloomfield’s health and performance assessment of each geolocated plant, one plant at a time through a web-based dashboard accessible via tablet, cellphone or computer. Bloomfield’s approach to inspecting and assessing plants contrasts sharply with the visual inspection which includes sparse subjective judgements of randomly sampled plant data.

This paper aims to contribute to the cold start problem in recommender system with Neural Network based approach. There are several attempts in academia and in the industry to improve the recommender system. For instance, latent matrix factorization is an algorithm that solves the recommendation problem, it produces efficient outcomes from the core problem. Latent factors are not directly observed but are inferred from other factors. It can be computed by assuming a specific number of such factors and then transforming the large user-item matrix into a smaller matrix based on previously assumed factors. These smaller matrices can be multiplied to reproduce a close approximation to the original user-item matrix using a technique called matrix factorization. Assuming that the matrix can be written as the product of two low-rank matrices, matrix factorization techniques seek to retrieve missing or corrupted entries. Matrix factorization approximates the matrix entries by a simple fixed-function — namely, the inner product — acting on the corresponding row and column latent feature vectors. Substituting a neural architecture for the inner product that learns from the data, improves recommendation problem and deals with the cold start problem

Objective: By the lack of initiative by force (Faith) Iron man lives. Iron defi ciency causes anemia, anemia causes dementia, Alzheimer dementia and Alzheimer’s produces cognitive impairment in memory produces bases. Well hear him. The Iron Will Alkaline, the answer is yes. Methodology: On the basis of Love and the use of Iron and its allies, which are the B vitamins, Vitamin C, E and vitamin A. It is necessary to remember that there is to try to fi ght the greatest sustenance Anemia in all its contrarestantes. Conclusion: The theory focuses on the oxygenation of the blood, which must be done, where the Warburg Alkaline Diet is demonstrated, among other factors it is necessary to emphasize the oxygenation that consists of the mental and physical, which is reduced in Sleeping correctly, Warburg Alkaline Diet, Drink Enough Water, Make Walks or Moderate Exercises, Comfort and Drink Iron, Vitamin C, Vitamin E, Complex B and Vitamin A. All this consists in Producing New Oxygen.

The problem of predicting a set of tensorial outputs based on inputs of tensor form has been receiving increasing attention in recent years. This problem arises in various areas of mathematical, statistical and computational sciences, and generalizes the case of the widely used scalar-on-scalar regression methods. In this paper, we develop a tensor-on-tensor re gression framework using a hybrid of convolutionary neural networks and lowrank tensor learning algorithms. Our proposed framework integrates several promising approaches which have been developed previously to tackle this problem and extends their domain of applica tions. In particular, we demonstrate the advantage of this framework in comparison with traditional methods through an example of predicting the third-order tensors which arises within the procedures required for performing the time-homogeneous top-K ranking algo rithm. Computational results are further provided which pertain to analysis of the U.S. stock market during the time period from January 1990 to December 2019