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Agriculture is essential for everyone to promote sustainable development, the farming, which combines image processing, artificial intelligence, Deep learning, and Internet of Things (IOT). World population incensing every day. Due to the rising demand in the Agriculture industry, the need to collectively improve a plants and growth its field is very useful. In this paper, it is important to maintain the crop during its initial time, and also at period of harvesting. The image processing and artificial networks are used as a different techniques to maintain the detecting the diseases on the leaves and correct time to harvesting. When we take images with help of drones, the images are divided and changed to disease described three things vectors namely the first one is color, one more is texture and morphology. The vectors morphology gives 95% accuracy and its give more compare to other two vector features. This research paper proposed effective and useful algorithms for detection of disease with help implementation of Artificial Neural network algorithms using MATLAB. Detection of leaf or plant diseases with some manual techniques are requires a lot of work by maintaining a huge farm of crops, and it's very early stages it detects different types of symptoms to different diseases on plants, when the displayed on crop leaves. In this research paper survey on various disease classification techniques that can be for plant leaves diseases detection. For this purpose Artificial Intelligence, Neural network algorithms and back propagation techniques for adjustment of training data sets. [ABSTRACT FROM AUTHOR]

The wireless networks of the future generation are evolved as complex systems due to broadening in service prerequisites, application heterogeneity and networking of gadgets. In recent times, the major step forward of machine learning techniques is deep learning. However, in wireless/heterogeneous networks, the deep learning application for network traffic control is relatively new. The key disputes in the wireless backbone networks since the advancement of wireless networks are resource allocation and efficient network traffic control such as routing. In larger scale of networks and tangled radio environments, the method of adding intelligence to wireless networks is achieved by Deep Learning (DL). The tangled wireless networks supported with several nodes and their quality of variable link can be investigated by Deep Learning. This paper is presented with a perception of harnessing the next generation communication networks with the artificial neural networks. This work helps the readers to explore the unsolved issues to pursue their research and deeply understand the wireless network design with DL based state of the art facilities. In this work, we integrate the deep learning and wireless networking research with a widespread survey. Finally, this paper summarizes the disputes and benefits of acquiring ML and AI for next generation wireless systems. [ABSTRACT FROM AUTHOR]

In this paper a hybrid approach for spectral analysis and voice profiles recognition by techniques on the base of Machine Learning and Artificial Intelligence (AI) have been proposed. For voice-processing procedures was applied the algorithm of Fast Fourier Transformation (FFT) with several different window types, respectively Hamming, 4 Term BHarris, Flat Top and Hanning. The spectral feature extraction is used for pre-processing training sets about k-Nearest Neighbours (k-NN) classification models and Feed-Forward Neural Networks (FFNN) for individual's personal identity about target group of people. Euclidean, Cityblock, Minkowski and Chebychev metric distances were applied in k-NN model creation. The design models are evaluated through resubstitution and cross-validation techniques. Levenberg- Marquardt learning algorithm was used to FFNN architectures with Linear and Tangent Sigmoid activation functionsin network outputs. High quality k-NN and FFNN models in regard to personal voice identification with level of accuracy achieved 97.68 % and 100.0 % were synthesized. [ABSTRACT FROM AUTHOR]