European Journal of Molecular & Clinical Medicine

This paper audits and talks about examination propels on "logical AI" in PC vision. We centre on a specific zone of the "Seeing People" (LAP) topical space: primary imitations and character investigation. Our point is to variety the computational knowledge and PC vision networks mindful of the significance of creating logical systems for PC helped dynamic applications, for example, robotizing enlistment. Decisions dependent on character attributes are being made routinely by human asset offices to assess the up-and-comers' ability of social inclusion and their capability of profession development. Be that as it may, deducing character attributes and, as a rule, the procedure by which we people structure a first impression of individuals, is profoundly emotional and might be one-sided. Past investigations have shown that knowledge machineries can figure out how to imitate human choices. In this paper, we go above and beyond and figure the issue of clarifying the choices of the models as methods for distinguishing what visual perspectives are significant, seeing how they identify with choices recommended, and potentially picking up knowledge into unfortunate negative inclinations. We structure another test on reasonableness of knowledge machineries for first impressions examination. We portray the setting, situation, assessment measurements and starter results of the opposition. Supposedly this is the first exertion regarding difficulties for logic in PC vision. Moreover, our test configuration involves a few other measurable and subjective components of oddity, including a "competition" setting, which joins rivalry and coordinated effort.

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.

Nowadays robots are incorporated in the job which are often difficult for humans and these robots can be used as an effective alternative for humans.Mobile robots have become a significant topic in the security field Several techniques have been introduced to work with mobile robots and security. Most of these methods are not capable of working in low visibility environments and need to be manually controlled by a person all the time. In this smart security system, a mobile robot acts as an e-patrol in both light and dark environments. The design and implementation of mobile robots consist of three subsystems: The obstacle avoidance,image capturing and alarm indication for theft prevention.By means of which bot can be deployed for surveillance in a defined path with specific intervals.

Skin types of diseases are most common among the globe, as people get skin disease due to inheritance, environmental factors. In many cases people ignore the impact of skin disease at the early stage. In the existing system, the skin disease are identified using biopsy process which is analyzed and medicinal prescribed manually by the physicians. To overcome this manual inspection and provide promising results in short period of time, we propose a hybrid approach combining computer vision and machine learning techniques. For this the input images would be microscopic images i.e histopathological from which features like color, shape and texture are extracted and given to convolutional neural network (CNN) for classification and disease identification. Our objective of the project is to detect the type of skin disease easily with accuracy and recommend the best and global medical suggestions.
This paper proposes a skin disease detection method based on image processing and machine learning techniques. The patient provides an image of the infected area of the skin as an input to the prototype. Image processing techniques are performed on this image and feature values are extracted and the classifier model predicts the disease. The proposed system is highly beneficial in rural areas where access to dermatologists are limited. For this proposed system, we use Pycharm based python script for experimental results.

Glioma Tumor is generally found in the brain and spinal cord. This tumor begins in glial cells that cover the nerve cells and control the function of that. The Glioma tumor is classified based on glial cells involved in the Glioma tumor formation. The tumor affects the normal activity of the patients such as loss of memory, difficulties in speech, confuse the identification of objects, and also causes difficulties to maintain the balance of the body. The early detection of Glioma tumor helps healthcare practitioners to suggest a suitable treatment for the disease. The detection of a Glioma tumor is a challenging task. Many types of approaches had been proposed by the researchers and academicians for accurately detecting the  Glioma tumor. Accurately detecting the brain tumor is still a big challenge. Because of recent advances in image processing and computer vision, healthcare professionals are using sophisticated disease diagnostic tools for disorders/disease prediction. The Neurosurgeons and Neuro-Physicians use the magnetic resonance imaging technique to identify multiple brain tumors. The approaches to computer vision play a significant role in the automated identification of different Brain tumors. This research paper explores the Convolutional neural network-based Faster R-CNN approach for the Glioma tumor detection using four pre-trained deep networks such as Alexnet, Resnet18, Resnet50, and Googlenet. The proposed approach of object detection as compared to other R-CNN approaches is more efficient and accurate having higher precision.  The proposed model detects the Glioma tumor with 99.9% accuracy. The pre-trained networks used to train the tumor detection model are Alexnet, Resnet18, and Resnet50, and Googlenet. As compare to Alexnet, resnet18, and Googlenet deep networks, the Resnet50 Pre-trained network performed well with higher accuracy of detection.

A large number of researches have been conducted in the field of AI. This paper is all about the enhancements made in this popular field. Making a machine that is able to understand the background ideas of the words is very essential as it can increase the chances of better translation as well as can execute conversations as humans do. In particular, this paper states the difference between the AI and the Non-AI tasks. The work is generated for new candidates coming in the area of AI as well as some issues related to AI are also talked about.

Today, Identification of good quality seeds or crops is a major challenge in India. It is sort out using Image Classification and Data Mining techniques. Picture classification is a favoured strategy for horticulture item identification since the item is unblemished from the cycle. The image is captured and the features are distracted based on their attributes. This is then clustered using K-means and Principal Component Analysis method to assign the grades based on the result.