Online ISSN: 2515-8260

Keywords : Neural Networks

Vision Based Alert System for Road Signs Detection

K. Hemalatha; D.Uma Nandhini; Karthika S

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 1872-1877

TOver recent years, there is a huge increase in road accidents which makes us take more surveillance actions to reduce road accidents. In recent due to researches there is a huge improvement in the field of deep learning and computer vision. Our project is mainly focused on developing a vision based alert system for drivers. We built the model with the help of convolution neural networks a sub field of deep learning and computer vision. We have taken road sign data and trained the model to detect 32 different road signs. The data has been collected from German road sign datasets which consists of 20000 images. We developed the learning model with Keras frame- work which is a high-level API. The Keras works on the Tensor Flow backend which is developed by Google. The Keras framework enables us to build a state-of-the-art model to detect the road sign. For developing the model and to pre-processes the image we have used python language which has a vast number of libraries for image computations and to build deep neural networks. The main aim of our project is to develop a vision based alert system for drivers which will help us to improve road safety. Our model will also help new learners to improve the driving experience.

Detection and Identification of Bogus Profiles in online Social Network using Machine Learning Methods


European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 395-400

Here current creation online social networks (OSNs) become more and more common and the social life of people has become more linked to these pages. They use OSNs to remain in finger with everyone else, distribute news, prepare dealings and still run their personal e-. Out of control of the OSN's evolution and the huge extent of their supporters 'individual developments, they have been attackers and impostors who take individual information, share fake news and disseminate vindictive exercises. Researchers in various fields began inspecting environmentally friendly techniques in order to perform abnormal activity and counterfeit money that is based on accounting and classification algorithms [1]. However, the use of stand-alone classification algorithms no longer yields a straightforward outcome, some of the factors that are manipulated by the account have a low influence or have no impact in the closing results. The paper proposes to use the SVM-NN as a modern algorithm to effectively identify suspected Twitter accounts and bots, to add four choices and to restrict measurements. Three laptop classification mastering algorithms were used to determine the actual or false identity of target accounts. They included the SVM, the Neural Network and our recently urbanized SVM-NN method that utilizes far less hardware but is still able to correctly identify about 98% of the money due to the training data set.


V. Praveena; P. Chinnasamy; P. Muneeswari; R. Ananthakumar; Bensujitha .

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2438-2445

-Plants are very necessary for the earth and for all living organisms. Plants maintain the atmosphere. Plant illness, an impairment of the traditional state of a plant that interrupts or modifies its very important functions. All species of plants, wild and cultivated alike, are subject to illness. These diseases occur totally on leaves, but some might also occur on stems and fruits. Leaf diseases are the foremost common diseases of most plants. Plant pathology is the science study of pathogens and environmental circumstances causing illnesses in crops. Organisms causing transmissible disease include fungi, oomycetes, bacteria, viruses, viroids, etc. The latest technique involves automated classification of diseases from plant leaf images neural networks persecution approach called hunting enhancement of microorganisms primarily focused on executing Neural system relies on planar basic principle. Throughout this article, classic neural network algorithms are used to detect and classify the areas infected with multiple illnesses on the plant leaves in order to increase the velocity and precision of the network. The region's increasing formula will improve the network's potency by searching and grouping seed points with prevalent feature extraction method characteristics. The scheduled methodology achieves greater precision in disease detection and classification.