Automatic screening of diabetic retinopathy (DR) is a well-identified area of research in the domain of computer vision. It is challenging due to structural complexity and a marginal contrast difference between the retinal vessels and the background of the fundus image. As bright lesions are prominent in the green channel, we applied contrast-limited adaptive histogram equalization (CLAHE) on the green channel for image enhancement. This work proposes a novel diabetic retinopathy screening technique using an asymmetric deep learning feature. The asymmetric deep learning features are extracted using U-Net for segmentation of the optic disc and blood vessels. Then a convolutional neural network (CNN) with a support vector machine (SVM) is used for the DR lesions classification. The lesions are classified into four classes, i.e., normal, microaneurysms, hemorrhages, and exudates. The proposed method is tested with two publicly available retinal image datasets, i.e., APTOS and MESSIDOR. The accuracy achieved for non-diabetic retinopathy detection is 98.6% and 91.9% for the APTOS and MESSIDOR datasets, respectively. The accuracies of exudate detection for these two datasets are 96.9% and 98.3%, respectively. The accuracy of the DR screening system is improved due to the precise retinal image segmentation.

A modern worldview has developed in rural methods, devices, and advances. Exactness in agribusiness is required to guarantee site-specific editing of administration, which incorporates soil supplement arrangements that are custom fitted to each crop’s needs. In spite of the fact that preparation is vital for expanding efficiency, it is vital to dissect the possibilities and impediments of soil as a premise for selecting the correct manure sort, amount, and application time to dodge compost utilization instability. Farmers’ dependence on instinct, trial and mistake, mystery, and assessing significantly includes major wasteful aspects such as efficiency misfortunes, asset squandering, and expanded natural defilement due to the complexity of deciding the perfect preparing extend. Agriculturists cannot successfully estimate the impacts of their choices on yield and the environment when utilizing these. This paper illustrates why manure regimes should be adjusted to meet the demands of certain crops and regions, as well as to safeguard the environment by reducing pollution caused by fertilizer and manure waste. A few soil-richness administration strategies, such as the utilization of versatile research facilities or imported gear, have confronted obstacles in terms of fetched, comfort of utilization, and adaption to the neighborhood environment. Other choices, such as sending soil to research facilities for testing, are badly designed, time-consuming, and conflicting. Based on the climate estimate, this thing should be suggested according to the development of an ANN and show the estimates of NPK supplement levels and offer the fitting compost treatment and application timing.

The objective of this study is to build a model for the classification of traffic signs available in the image into many categories using a CNN and Keras library to detect the traffic sign. The goal of the traffic sign recognition is to build a deep neural network (DNN), which is used to classify traffic signs. The authors suggest training the model so it can decode traffic signs from natural images using the German Traffic Sign Dataset. This data should be firstly preprocessed in order to maximize the model performance. After choosing model architecture, fine tuning, and training, the model will be tested on new images of traffic signs found on the web. Because it deals with image classification, a convolutional neural network is chosen as a type of DNN, which is a common choice for this type of problem. The code is written in Python with use of tensor flow library. The proposed CNN model identifies traffic signs and classifies them with 95% accuracy. GUI of this model makes it easy to understand how signs are classified into several classes.

The Speech is basic part of a human being to communicate with each other. This paper demonstrate the analyzation of gender by the speech and present the certain emotions impact on fundamental pitch range. It differs from gender to gender. In Future speech will be major part to communicate with machine. Now a days as a learner we try to communicate machine with soft voice. This paper take a challenge accepting voice sample with different mood or emotion and detect a specific gender depending on the frequency of the gender.