Thir paper presents a new techniqzre for face recognition that can cope with partial occlussion or shong vuriations in facial expression. The method hies to solve the problem Pom near- holistic perspective. main idea is eliminate some features which may came reduction of accuracy tinder occlwion expression changing. To test and evaluate performance technique. series experiments have been carried out shown improved pei$onnance robustness when compared classical PCA.

We conducted an eye-tracking user study with 13 participants to investigate the influence of stimulus-question ordering and question modality on using visual question-answering (VQA) tasks. examined cognitive load, task performance, gaze allocations across five distinct experimental designs, aiming identify setups that minimize burden participants. The collected performance data were analyzed quantitative qualitative methods. Our results indicate a significant impact load as well noteworthy...

Face recognition based on 3D techniques is a promising approach since it takes advantage of the additional information provided by depth which makes whole more robust against illumination and pose variations. However, these approaches require cooperation person to acquire accurate data; thus, they are not appropriated for some applications such as video surveillance or restricted area access points where only 2D face image disposable. In this paper, novel presented data in training stage but...

This paper presents a novel face recognition approach which uses only partial information in the stage. The algorithm is based on an extension of classical PCA and called (P2CA). P/sup 2/CA combined 2D-3D scheme requires 3D data training process but can 2D pictures strategy has been proven to be very robust pose variation scenarios showing that retains all spatial while picture effectively recovers from available data. Simulation results with multi-view database have shown rates above 92%...

Recently, the demand for computer vision techniques is continuously rising because of development in decision making pertaining to health sector. Image processing a subset which makes use algorithms perform emulation recognize objects. In this study novel convolutional neural network configured based on deep learning classifying Chest x-ray images into five major classes. It addresses an issue insufficiency medical employing image classification. A new augmentation technique superimposing...

RPCs have to work. Given the current repute of homogeneous principle, structures engineers famously preference right unction Smalltalk and telephony. We describe a technique for sturdy epistemologies (Crotch), which we use illustrate that RAID can collude answer this obstacle. in critiques many, Crotch permits scatter/acquire I/O. at same time as additionally it is private in-tent, it's miles burette by means way previous work inside field. drawback shape solution, but, famous collaborative...

Aim: This research work aims at developing an automatic medical image analysis and detection for accurate classification of brain tumors from a magnetic resonance imaging (MRI) dataset.We developed new MIDNet18 CNN architecture in comparison with the AlexNet classifying normal images tumor images. Materials methods:The novel comprises 14 convolutional layers, seven pooling four dense one layer.The dataset used this study has two classes: MR images.This binary MRI consists 2918 as training...

Currently, Lung diseases are the major problem that affect lungs which is an important organs allow us to survive through breathing. The such as pleural effusion, Asthma, chronic bronchitis, and normal lung detected classified in this work. This paper presents a Computer Tomography (CT) Images of for detection developed using ANN-BPN. purpose work detect classify by effective feature extraction Dual-Tree Complex Wavelet Transform GLCM Features. entire segmented from parameters calculated...

The lung is one of the prime organs, and any disease in causes mild to severe breathing problems; untreated will lead several complications. Tuberculosis (TB) a ailment that needs premature recognition handling. primary objective employ deep-learning (DL) based TB detection using chest <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> -rays. Various stages proposed scheme consist (i) data collection resizing, (ii) DL-supported feature...

Implementing image processing tools demands its components produce better results in critical applications like medical classification. TensorFlow is one open source with a machine learning framework for high performance and operates heterogeneous environments. It heralds broad attention at fine tuning of parameters obtaining the final models, to obtain performance. The main aim this article prove appropriate steps classification techniques diagnosing diseases accuracy. proposed...

Maintaining good Oral Health (OH) is important for an individual's general health. Clinical-level examination of the OH using recommended protocol time consuming, and hence computerized algorithm-supported methods are commonly adopted in recent years. Owing to its increased accuracy, Deep Learning (DL) based testing has become a popular procedure times. This study suggests revolutionary Unified DL (UDL) technique that uses digital photos taken from actual patients assess state teeth. With...

The ultimate purpose of this investigation is to carry out an efficient classification handwritten medical prescription recognition by utilising a one-of-a-kind CRNN Algorithm, and then evaluate its level performance in contrast that Alexnet. A total ten representatives from each group's sample were considered for the analysis. Under context specific experiment, Alexnet Novel algorithm study groups are taken into consideration. Calculations performed using Gpower calculator, confidence range...

Deep-learning (DL) applications that are used real-time across various industries have gained a lot of traction and become increasingly popular, especially when it comes to data-driven recommendation systems. This work aims develop DL scheme support the music-recommendation system (MS) based on music data. The phases this includes; (i) data collection signal-image conversion get necessary RGB scale images from data, (ii) pre-trained feature extraction, (iii) deep-features detection recommend...

Breast cancer is considered a severe illness in the female society, and if left untreated, it can be fatal. It always desirable to detect BC early utilizing selected imaging strategy. Thermogram supported breast abnormality detection one of recent technique this gives necessary information form distributed thermal pattern. This research aims implement Convolutional-Neural-Network (CNN) based segmentation extract region from chosen thermogram. scheme's multiple stages include: (i) data...

The aim of the study was to introduce Novel Ridge Regularization model for effective prediction COVID-19 cases and its impact on people's livelihood therefore by reducing overfitting data. In this two groups were used classification namely regularization with sample size 110 SVM (Support Vector Machine) technique 110, [8] similarly dataset 1024 experiment. Based experiment it observed that ridge has got Least RMSE values than significance p=0.032. provides a better approach analyzing model.

Human Activity Recognition is a technique for classifying person's activity using sensitive sensors that are influenced by movement. Improving the performance of based on information sensed smartphones. In this we have considered two groups namely forest optimization with sample size 111 and novel deep ensemble 111. Accuracy computed data set 428 to recognize different human Activities (Hand Waving, running). It was observed Forest Optimization Algorithm obtains 95.75% loss 12.6%. appears...

Views Icon Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Twitter Facebook Reddit LinkedIn Tools Reprints and Permissions Cite Search Site Citation S. Sreedhar, A. Rama; Ridge regression for analyzing the impact of Covid-19 on social mass media comparing with support vector machines. AIP Conf. Proc. 7 May 2024; 2853 (1): 020197. https://doi.org/10.1063/5.0197611 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote...

The main achievement of this work is the development a new face recognition approach called partial principal component analysis (P/sup 2/CA), which exploits novel concept using only information for stage. This uses 3D data in training stage but it permits to use either 2D or stage, making whole system more flexible. Preliminary experiments carried out on multi-view database composed 18 individuals have shown robustness against big pose variations obtaining higher rates than conventional PCA...

This paper proposes a convolutional neural network for diagnosing various lung illnesses from chest CT images based on customized Medical Image Analysis and Detection (MIDNet18). With simplified model building, minimal complexity, easy technique, high-performance accuracy, the MIDNet-18 CNN architecture classifies binary multiclass medical images. Fourteen layers, 7 pooling 4 dense 1 classification layer comprise architecture. The image process involves training, validating, testing model....

In this paper we tend to use machine learning algorithms like SVM, KNN and GIS perform a behavior comparison on the net pages classifications drawback, from experiment see within SVM with tiny range of negative documents make centroids has littlest storage demand also least line take look at computation value. However most completely different nearest neighbors have fair higher value than KNN. This means that some future work ought be done do cut back list GIS.Keywords: Net Classifications,...