Strategies and Approaches for Generating Identical Extensive XML Tree Instances

In recent years, XML has become the de facto internet wire language. Data may be organized and given context with the use of XML. A well-organized document facilitates the transformation of raw data into actionable intelligence. In B2B1 applications, the XML data is sent and created. This implies the need for fast query processing on XML data. The processing of XML tree sample queries (XTPQ) that provide an efficient response (also known as sample matching) is a topic of active study in the XML database field.DOM (Parser) may be used to transform an XML document into a tree representation. Extensible Markup Language (XML) query languages like XPath and XQuery use tree samples (twigs) to express query results.XML query processing focuses mostly on effectively locating all instances of twig 1 samples inside an XML database. Numerous techniques for matching such tree samples have been presented in recent years. In this study, we survey recent developments in XTPQ processing. This summary will begin by introducing several algorithms for twig sample matching and then go on to provide some background on holistic techniques to process XTPQ

Security Issues in Service Model of Fog Computing Environment

Fog computing is an innovative way to expand the cloud platform by providing computing resources. The platform is a cloud that has the same data, management, storage and application features, but their origins are different because they are deployed to different locations. The platform system can retrieve a large amount, work in the field, be fully loaded, and mount on a variety of hardware devices. With this utility, Fog Framework is perfect for applications and critical moments. Fog computing is similar to cloud computing, but because of its variability, creates new security and privacy challenges that go beyond what is common for fog nodes. This paper aims to understand the impact of security problems and how to overcome them, and to provide future safety guidance for those responsible for building, upgrading and maintaining fog systems

IoT-Based System for Automated Accident Detection and Rescue

One of the most common causes of death is auto accidents. The worst thing that may happen to a road user is a traffic accident, despite the fact that they happen regularly. The worst part is that we fail to learn from our on-the-road errors. Most people who use roads regularly are extremely familiar with the fundamental guidelines and safety procedures that should be followed, but it is only their own negligence that results in accidents and wrecks. Accidents and crashes are primarily caused by human error. Here are some examples of normal human actions that result in accidents. 1. Driving too fast; 2. Driving while intoxicated; 3. Distracting the driver; 4. Running red lights; 5. Avoid utilizing safety equipment, such as seatbelts and helmets; 6. Driving erratically and overtaking improperly in order to save lives in a traffic collision, we’re going to construct an Arduino-based car accident alert system that combines GPS, GSM, and an accelerometer. If the accelerometer detects an abrupt shift in the vehicle’s axis, the GSM module alerts you and communicates the location of the accident to your cell phone. The GPS module’s latitude and longitude are utilized to pinpoint the accident’s location, which is provided as a Google Map link. The message also contains the vehicle’s speed in knots

Feed forward Neural Networks for Accurate Thyroid Detection in Healthcare

Clinical procedures, which require a large number of personnel and medical resources, receive the majority of the current focus on thyroid nodule diagnosis. An automated thyroid ultrasound nodule identification system is built using image texture data and convolutional neural networks in this study. The following are the major phases: The underlying stages in building a ultrasound thyroid knob dataset incorporate gathering positive and negative examples, normalizing pictures, and portioning the knob region. Second, a texture features model is built by selecting features, reducing the dimensionality of the data, and extracting texture features. Third, deep neural networks in move learning are utilized to create an element model of the knob in an image. The convolutional brain network highlight model and the surface component model were combined to create the brand-new knob include model known as the Feature Fusion Network. The Feature Fusion Network is used to prepare and improve performance over a single organization in order to create a demonstrative model for deep neural networks that can adapt to a variety of knob features. 1874 clinical ultrasonography thyroid knobs were gathered for this investigation. The musical normal F-score considering Accuracy and Review is utilized as an assessment metric. With an F-score of 92.52 percent, the study’s findings suggest that the Element Combination Organization can differentiate between benign and harmful thyroid knobs. As far as execution, this methodology performs better compared to standard ML procedures and convolutional neural networks

Enhancing Impulsive Hatred Detection with Ensemble Techniques and Active Learning

The increasing propagation in recent years of hatred on social media and the dire requirement for counter measures have drawn critical speculation from state run administrations, organizations, and analysts. Despite the fact that specialists have observed that disdain is an issue across different Social media stages, there is an absence of models for online disdain location utilizing this multi-stage information. Different techniques have been produced for robotizing disdain discovery on the web. Here we will begin by giving the current issue that comes the right to speak freely of discourse on the Internet and the abuse of virtual entertainment stages like Twitter, as well as distinguishing the holes present in the current works. At long last, figured out how to tackle these issues. It is a considerably more testing task, as examination of the language in the common datasets shows that disdain needs one of a kind, discriminative highlights and in this manner making it challenging to find. Removing a few exceptional and significant elements and joining them in various sets to look at and dissect the presentation of different machine learning classification calculations as to each list of capabilities. At long last, subsequent to leading a top to bottom investigation, results show that it is feasible to fundamentally expand the classification score acquired

Feasible Prediction of Diabetes in Pregnant Woman and Neonatal Mellitus in New Born Child using Machine Learning

Diabetes during pregnancy is a major source of health problems in unborn infants and their moms. Because gestational diabetes can develop to permanent diabetes, ML is an important method for predicting the likelihood of such progression based on the given features. Although the current study may predict lifelong diabetes in pregnant women, it cannot predict the likelihood of neonatal diabetes. As a result, new characteristics are required to improve the forecasting of neonatal mellitus and provide the most accurate and feasible diabetes persistence results in pregnant women. Python scripting and the application of Machine Learning methods such as SVM, KNN, and LR can assist in achieving this aim. The preprocessing ML dataset focusing on Diabetes from the Pima Indian diabetes database collected through Kaggle. In addition, two new attributes were added to the paper’s dataset. According to research, machine learning models using characteristics like SVM and decision trees may successfully predict the risk of diabetes in pregnant women. Various factors have been used to predict the beginning of this condition during pregnancy

Breast Cancer Diagnosis from Histopathology Images Using Deep Learning Methods: A Survey

Breast cancer is a major public health issue that may be remedied with early identification and efficient organ therapy. The diagnosis and prognosis of severe and serious illnesses are likely to be followed and examined by a biopsy of the affected organ in order to identify and classify the malignin cells or tissues. The histopathology of tissue is one of the major advancements in modern medicine for the identification of breast cancer. Haematoxylin and eosin staining slides are used by pathologists to identify benign or malignant tissue in clinical instances of invasive breast cancer. A digital whole slide imaging (WSI) is a high-resolution digital file that is permanently stored in memory for flexible use. This article will look at and compare how breast cancer cells are categorised manually and automatically. lobular carcinoma in situ and ductal carcinoma in situ are the two types of breast cancer. Here, detailed explanations of numerous techniques utilised in histopathology pictures for nucleus recognition, segmentation, feature extraction, and classification are given. The pre-processed image is utilised to extract the nucleus patch using several feature extraction approaches. Thanks to the great computational capability of the general processing unit (GPU), algorithms may be implemented effectively and efficiently. Deep Convolution Neural Network (DCNN), Support Vector Machines (SVM), and other machine learning methods are the most popular and effective computer algorithms