Age and gender that are the two key facial attributes, play a foundational role in social interactions, making age and gender estimation from one face image a crucial task in intelligent applications, like access control, human-computer interaction, enforcement, marketing intelligence and visual surveillance. The basic aim of this paper is to develop an algorithm that estimates age and gender of a person correctly. One of the most widely used techniques is haar cascade. In this paper we propose a model which can predict the gender of a person with the assistance of Haar Cascade. The model trained the classifier with different male and female images as positive and negative images. Different facial features are extracted. With the assistance of Haar Cascade classifier will determine whether the input image is male or female. We made use of Deep-Convolution neural network. It works efficiently even with limited data. For the age approximation task, the paper makes use of caffedeep learning framework. Caffe provides expressive architecture, extensible code. Caffe can process over 60M photos per day. This makes it one of the fastest convent implementation available.
The rise of incidences of melanoma skin cancer is a global health problem. Skin cancer, if diagnosed at an early stage, enhances the chances of a patient’s survival. Building an automated and effective melanoma classification system is the need of the hour. In this paper, an automated computer-based diagnostic system for melanoma skin lesion classification is presented using fine-tuned EfficientNetB3 model over ISIC 2017 dataset. To improve classification results, an automated image pre-processing phase is incorporated in this study, it can effectively remove noise artifacts such as hair structures and ink markers from dermoscopic images. Comparative analyses of various advanced models like ResNet50, InceptionV3, InceptionResNetV2, and EfficientNetB0-B2 are conducted to corroborate the performance of the proposed model. The proposed system also addressed the issue of model overfitting and achieved a precision of 88.00%, an accuracy of 88.13%, recall of 88%, and F1-score of 88%.
The sharp increase within the quantity of wastage in terms of food makes the requirement for charity in terms of donation. within the current situation food is being wasted daily on an outsized basis in numerous restaurants, weddings, social functions, faculty canteens and plenty of different social events. individuals give food manually by visiting every organization range of times so as to cut back the issues of food wastage whereas their area unit websites that have taken efforts to assist individuals give food. The planned system presents, a replacement internet-based application that gives a platform for donating leftover food to all or any poverty-stricken people/organizations. The system is shown to be an efficient suggests that of donating things to organizations, etc. over the web. It shows the potential for avoiding the wastage of food. It provides data regarding the motivation to return up with such associate application, thereby describing existing donation system and the way the merchandise works for betterment of the society. this technique can produce a standard collaboration portal for hotels/restaurants and charities, charity will directly contact edifices who have food remaining and report generation which can show what quantity food is given by that restaurant and providing reward points for them .In this system Food Donor, Food receiver, Third party merchandiser, admin and premium user area unit the most modules wherever Food Donor may be any organization, institute or faculty who desires to give food and build a replacement food donation request and Food receiver may be any charity firm seeking for food. a replacement food donation request are going to be created on the portal and once the request is accepted, a notification is distributed.
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