Object detection and gender recognition are the two different categories to be classified in a single section is a complicated task and needs to support the blind people.In this paper our method to better sensation of a blind persons by conversion of visualized data to audio data.Therefore the artificial intelligence model requires to detect the objects as well as human face recognition with gender classification algorithms. This model processed with feature extraction and classification models. The feature extraction was comprised with multi scale invariant feature transform(MSIFT), with feature optimization with support vector machine algorithm then classified using LASSO classifier. For better performance identification three different classification models were implemented and tested too. Feature selection helps in making tests early to detect the objects and recognising human actions using image processing approach. This can be applied for both offline and online modes. But in this scenario offline mode was implemented and was tested with combination of different databases. For this process of classification ridge regression (RR), elastic net (EN), lasso regression(LR) and lasso regression were implemented.The final classfication results with accuracy are as follows for RR- 89.6%, EN- 93.5%, LR-93.2% and propsed approach(LRGS) with 98.4% accurate detection rate with prediction name of classes.

Object detection and gender recognition were two different categories to be classified in a single section is a complicated task and this approach helps in supporting the blind people for an artificial vision. In this paper, our method to the betters vision sensation of blind persons by conversion of visualized data to audio data. Therefore this artificial intelligence model helps in detecting the objects as well as human face recognition with gender classification based on face recognition approach. This model processed with feature extraction and classification models. The feature extraction was comprised with multi scale-invariant feature transform (MSIFT), with feature optimization with support vector machine algorithm then classified using LASSO classifier. For better performance identification, three different classification models were implemented and tested too. Feature selection helps in making tests early to detect the objects and recognizing human actions using image processing approach. This approach can be applied for both offline and online modes. But in this scenario, an offline mode was implemented and was tested with a combination of different databases. For this process of classification ridge regression (RR), elastic net (EN), lasso regression (LR) and LASSO regression were implemented. The final classification results with accuracy are as follows for RR-89.6%, EN-93.5%, LR-93.2% and proposed approach (LRGS) with 98.4% accurate detection rate with prediction name of classes.

Object detection and gender recognition are the two different categories to be classified in a single section is a complicated task and needs to support the blind people. In this paper, our method to the better sensation of blind persons by conversion of visualized data to audio data. Therefore the artificial intelligence model requires to detect the objects as well as human face recognition with gender classification algorithms. This model processed with feature extraction and classification models. The feature extraction was comprised with multi scale-invariant feature transform ( MSIFT), with feature optimization with support vector machine algorithm then classified using LASSO classifier. For better performance identification, three different classification models were implemented and tested too. Feature selection helps in making tests early to detect the objects and recognizing human actions using image processing approach. This approach can be applied for both offline and online modes. But in this scenario, an offline mode was implemented and was tested with a combination of different databases. For this process of classification ridge regression (RR), elastic net (EN), lasso regression(LR) and LASSO regression were implemented. The final classification results with accuracy are as follows for RR- 89.6%, EN- 93.5%, LR-93.2% and proposed approach(LRGS) with 98.4% accurate detection rate with prediction name of classes.

Among all cancers, pancreatic cancer has a very poor prognosis. Early diagnosis, as well as successful treatment, are difficult to achieve. As the death rate is increasing at a rapid rate (47,050 out of 57650 cases), it is of utmost importance for medical experts to diagnose PC at earlier stages. The application of Deep Learning (DL) techniques in the medical field has revolutionized so much in this era of technological advancement. An analysis of clinical proteomic tumor data provided by the Clinical Proteome Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma (CPTAC-PDA) at the National Cancer Institute was used to demonstrate an innovative deep learning approach in this study. This includes a) collection of data b) preprocessed using CLAHE and BADF techniques for noise removal and image enhancement, c) segmentation using UNet++ for segmenting regions of interest of cancer. Followed by, d) feature extraction using HHO based on CNN and e) feature selection using HHO based on BOVW for extracting and selecting features from the images. Finally, these are subject to the f) classification stage for better analysis using the VGG16 network. Experimental results are carried out using various state-of-art models over various measures in which the proposed model outperforms with better accuracy:0.96, sensitivity:0.97, specificity:0.98, and detection rate:0.95.Povzetek: Opisana je metoda globokega učenja za napovedovanje raka na ledvicah.

The blind people has their difficulty to identify the object moving around them, therefore with a high accuracy score object detection and human face recognition system will helps them in identifying the things around them with ease. Facial record images are immobile an difficult assignment for biometric authentication systems due to various types of characteristics are dimensions, pose, expressions, illustrations and age etc. In facial and other united images includes different objects classifications. In this research article, a minimum distance trainer for feature selection by accessing SVM feature optimization process. For feature selection process SVM (support vector machine) was considered for improving its feature interpretability and computational efficiency., then LASSO classifier applied to perform object recognition and gender classification. Original face image database used for the gender classification. This approach was implemented with dual classification model (1) Recognizing or classifying human faces from various objects and (2) Classifying gender through face recognition] is made possible with the help of combining modified SIFT feature in combination with ridge regression (RR), elastic net (EN), lasso regression(LR) and lasso regression with Gaussian Support Vector Machines (LRGS) based classification.