No abstract

This paper proposes a new type of Artificial Neural Network called Auto-Resonance Network (ARN) derived from synergistic control of biological joints. The network can be tuned to any real valued input without any degradation of learning rate. Neuronal density of the network is low and grows at a linear or low order polynomial rate with input classification. Input coverage of the neuron can be tuned dynamically to match properties of input data. ARN can be used as a part of hierarchical structures to support deep learning applications.

No abstract

No abstract

Today, agriculture has become more of science than art. The demand for fresh produce is growing, while availability of field workers is diminishing. Also, Farmers keep investing in land, irrigation, and labor, and much of the fruit is left hanging on the trees because there are not enough people to pick it when it is ripe. Automation in the agricultural field using modern information and Communication Technology can increase the efficiency, reliability, and precision; and reduces the need of human intervention. Thus, to meet solution to this problem is by reducing the costs for farmers and increasing the yield of crops which can be achieved by classifying of fruit by using modern technique in the field of Deep Neural Networking that involves CNN, YOLO etc. which provides a helping hand to farmers to optimize their farming patterns and boost yields by allowing them to grow taller fruit trees that can be harvested by classifying based on the condition of fruit and saving significant labour cost. In conclusion, the proposed methods are highly acceptable and recognizing the necessity, and unresolved classifications.