Data Sharing based on Facial Recognition Clusters

The evolution of computer vision technologies has led to the emergence of novel applications across various sectors, with face detection and recognition systems taking center stage. In this research paper, we present a comprehensive examination and implementation of a face detection project that harnesses the cutting-edge face recognition model. Our primary aim is to create a reliable and effective system that can be seamlessly integrated into functions allowing users to input their image to capture their facial features, subsequently retrieving all images linked to their identity from a database. Our strategy capitalizes on the dlib library and its face recognition model, which com- bines advanced deep learning methods with traditional computer vision techniques to attain highly accurate face detection and recognition. The essential elements of our system encompass face detection, face recognition, and image retrieval. Initially, we employ the face recognition model to detect and pinpoint faces within the captured image. Following that, we employ facial landmarks and feature embeddings to recognize and match the detected face with entries in a database. Finally, we retrieve and present all images connected to the recognized individual. To validate the effectiveness of our system, we conducted extensive experiments on a diverse dataset that encompasses various lighting conditions, poses, and facial expressions. Our findings demonstrate exceptional accuracy and efficiency in both face detection and recognition, rendering our approach suitable for real-world applications. We envision a broad spectrum of potential applications for our system, including access control, event management, and personal media organization

Intelligent smart farming and crop visualization

Agriculture is a backbone of the economy for any country. Being a part of primary sector, all the other major sectors and industries depend on it for their raw materials. It satisfies the basic needs of human like food, clothing and shelter. However, due to climate change and other related problems, it is becoming increasingly difficult for farmers to keep pace with rising demands. As per estimate by Food and Agricultural Organization of United Nations, around 55 percent of India’s total land area is used for agricultural produce. India is also a leading producer and exporter of some of the major crops. Still there are concerns regarding food security in India by United Nations. For overcoming the natural hurdles, involvement of technology is required for better analysis and decision-making. Through this paper, we plan to propose a visualization technique, which can help farmers to make better decision regarding crop selection. The study proposes a novel framework where farmers can get detailed information about the crops grown in any particular district and also area, production and productivity of any particular crop. This web-based agri solution will help farmers to take smart farming decision by resource optimization and smart planning

Pancreas of coxsackievirus-infected dams and their challenged pups: A complex issue

Enteroviral infections are frequent, often asymptomatic in humans and during gravidity. The present study is an extension of our previous investigations where we had shown pancreatitis in challenged pups of CVB4-E2-infected dams. Present investigation describes the effect of gestational infection with this virus on the pancreas of both dams and their challenged pups. Gravid CD1 outbred mice were orally infected with CVB4-E2 virus at different gestation times. Pups were challenged orally with the same virus after 25 days of birth. Organs were collected at selected intervals postinfection (p.i.), and replicating virus and viral-RNA copies were analyzed. Additional readouts included histopathology and immunohistochemical (IHC) analysis for localization and identification of Ly6G+ cells (neutrophils), CD11b+ cells (macrophages), and viral protein in pancreatic tissue sections of the infected dams and their challenged pups. Our results show the presence of replicating virus in the pancreas of infected dams and their challenged pups, with inflammation leading to chronic necrotizing pancreatitis and atrophy of pancreatic acini of the dams and their offspring. IHC analysis of the infiltrating cells showed pronounced Ly6G+ neutrophils in dams only, whereas CD11b+ macrophages were present in tissues of both, the pups and the dams. Time of infection during gravidity as well as the p.i. intervals when mice were sacrificed influenced the pancreatic pathophysiology in both groups. We conclude that coxsackievirus infection during pregnancy is a risk factor for chronic affliction of the exocrine tissue and could affect endocrine pancreas in the mother and child

β1-Adrenergic Receptor Contains Multiple IA\u3csup\u3ek\u3c/sup\u3e and IE\u3csup\u3ek\u3c/sup\u3e Binding Epitopes That Induce T Cell Responses with Varying Degrees of Autoimmune Myocarditis in A/J Mice

Myocarditis/dilated cardiomyopathy (DCM) patients can develop autoantibodies to various cardiac antigens and one major antigen is β1-adrenergic receptor (β1AR). Previous reports indicate that animals immunized with a β1AR fragment encompassing, 197–222 amino acids for a prolonged period can develop DCM by producing autoantibodies, but existence of T cell epitopes, if any, were unknown. Using A/J mice that are highly susceptible to lymphocytic myocarditis, we have identified β1AR 171–190, β1AR 181–200, and β1AR 211–230 as the major T cell epitopes that bind major histocompatibility complex class II/IAk or IEk alleles, and by creating IAk and IEk dextramers, we demonstrate that the CD4 T cell responses to be antigen-specific. Of note, all the three epitopes were found also to stimulate CD8 T cells suggesting that they can act as common epitopes for both CD4 and CD8 T cells. While, all epitopes induced only mild myocarditis, the disease- incidence was enhanced in animals immunized with all the three peptides together as a cocktail. Although, antigen-sensitized T cells produced mainly interleukin-17A, their transfer into naive animals yielded no disease. But, steering for T helper 1 response led the T cells reacting to one epitope, β1AR 181–200 to induce severe myocarditis in naive mice. Finally, we demonstrate that all three β1AR epitopes to be unique for T cells as none of them induced antibody responses. Conversely, animals immunized with a non-T cell activator, β1AR 201–220, an equivalent of β1AR 197–222, had antibodies comprising of all IgG isotypes and IgM except, IgA and IgE. Thus, identification of T cell and B cell epitopes of β1AR may be helpful to determine β1AR-reactive autoimmune responses in various experimental settings in A/J mice

Epitope Mapping of SERCA2a Identifies an Antigenic Determinant That Induces Mainly Atrial Myocarditis in A/J Mice

Sarcoplasmic/endoplasmic reticulum Ca2+ adenosine triphosphatase (SERCA)2a, a critical regulator of calcium homeostasis, is known to be decreased in heart failure. Patients with myocarditis or dilated cardiomyopathy develop autoantibodies to SERCA2a suggesting that they may have pathogenetic significance. In this report, we describe epitope mapping analysis of SERCA2a in A/J mice that leads us to make five observations: 1) SERCA2a contains multiple T cell epitopes that induce varying degrees of myocarditis. One epitope, SERCA2a 971–990, induces widespread atrial inflammation without affecting noncardiac tissues; the cardiac abnormalities could be noninvasively captured by echocardiography, electrocardiography, and magnetic resonance microscopy imaging. 2) SERCA2a 971–990-induced disease was associated with the induction of CD4 T cell responses and the epitope preferentially binds MHC class II/IAk rather than IEk. By creating IAk/and IEk/SERCA2a 971–990 dextramers, the T cell responses were determined by flow cytometry to be Ag specific. 3) SERCA2a 971–990-sensitized T cells produce both Th1 and Th17 cytokines. 4) Animals immunized with SERCA2a 971–990 showed Ag-specific Abs with enhanced production of IgG2a and IgG2b isotypes, suggesting that SERCA2a 971–990 can potentially act as a common epitope for both T cells and B cells. 5) Finally, SERCA2a 971–990-sensitized T cells were able to transfer disease to naive recipients. Together, these data indicate that SERCA2a is a critical autoantigen in the mediation of atrial inflammation in mice and that our model may be helpful to study the inflammatory events that underlie the development of conditions such as atrial fibrillation in humans

An analysis of mobile pass-codes in case of criminal investigations through social network data

In today’s modern world, mobile has turned out to be one of the essentials for all the people irrespective of their status and profession. With the help of this device, all the data about a person can be tracked down (i.e. from the diet, diseases to contacts and transactions). In case of criminal investigations, inspectors need to collect information about a victim or accused. For this, the individual’s mobile phone plays a vital role. However, it is very difficult to access a device without its owner’s permission. Suppose, if the victim is dead or not willing to expose the information, the cyber police should perform many complex tasks to retrieve the data. Thus, there is a great need to analyze this task and make it feasible to find out pass-codes in order to access the mobile device. This paper explains how passwords can be cracked with ease with the help of survey and training of large dataset. We all know that people these days are very active on social media, which makes it easy to track them. In this work, we analyze a few passcodes and patterns and try to test that data by giving the queries. Here, we can analyze and retrieve the data from an individual’s social media such as date of birth, name, personal information and try to predict the passcode in very few attempts as it turns out that majority of the time, the passcode is generally predictable based on some key characteristics identified in this paper

A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease

Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). Glomerular Filtration Rate (GFR) and kidney damage markers are used by researchers around the world to identify CKD as a condition that leads to reduced renal function over time. A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage in order to prevent the disease. This research presents a novel deep learning model for the early detection and prediction of CKD. This research objectives to create a deep neural network and compare its performance to that of other contemporary machine learning techniques. In tests, the average of the associated features was used to replace all missing values in the database. After that, the neural network’s optimum parameters were fixed by establishing the parameters and running multiple trials. The foremost important features were selected by Recursive Feature Elimination (RFE). Hemoglobin, Specific Gravity, Serum Creatinine, Red Blood Cell Count, Albumin, Packed Cell Volume, and Hypertension were found as key features in the RFE. Selected features were passed to machine learning models for classification purposes. The proposed Deep neural model outperformed the other four classifiers (Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic regression, Random Forest, and Naive Bayes classifier) by achieving 100% accuracy. The proposed approach could be a useful tool for nephrologists in detecting CKD