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Heart disease is quickly becoming one of the most critical and widespread health concerns in the globe at the present time. At this time, a great deal of research is being conducted in an effort to treat a variety of cardiac conditions and a great number of lives. Because it enables medical practitioners to begin treating patients as soon as possible, early diagnosis of any heart illness is critical for this purpose. The purpose of this research is to develop a method that is capable of detecting coronary disease even before the onset of symptoms. After acquiring a data set consisting of highly processed ECG signal levels and other parameters that are required for the diagnosis of a variety of heart-related conditions, anomalies and outliers were eliminated from the data. One part of the dataset consisted of inputs classified as a category, whereas the other part consisted of inputs classified as binary. After that, we used the Keras framework to construct a deep learning model with three layers, and then we fed it the data set. The accuracy of the model with the binary data set was 93.89 percent, which is satisfactory given the modification made to this data set to include cancer cases in adults aged 18 to 77. In addition to the artificial neural network, a K-Fold Cross Validation model was developed and data was input into it. The accuracy of this model in diagnosing heart illness was 91.6 percent, which is quite good and helps in recognizing anomalies. This model also had a high success rate in identifying heart disease. As a result of advancements in technology, we are now in a position to diagnose illnesses and abnormalities of the heart in a greater population with greater precision [ABSTRACT FROM AUTHOR]

Retinal fundus images may be used to conduct an automatic diagnosis of a variety of illnesses, including diabetic retinopathy, hypertension, and others. Identification of blood vessels is the most common stage in retinal fundus image analysis. Blood vessel segmentation in fundus images has been extensively studied and documented. The challenge has been solved using a variety of automated and quasi-image processing approaches. On retinal fundus images, this article shows a blood vessel segmentation approach. The vessels are enhanced and contrasted with other vessels, such as pixel structures, using a pre-processing phase. To constantly add additional vessel pixels to the segmentation output, an iterative refinement approach is used in which region growth is combined with vessel criteria. Sensitivity and specificity measurements are used to evaluate segmentation performance. The suggested technique offers excellent accuracy and minimal computing complexity, according to experimental findings on the DRIVE and STARE datasets. As a result, it may be used in medical diagnosis software applications. [ABSTRACT FROM AUTHOR]

In Modern era the Internet has play a vital role and called as modern revolution in terms of the speed and scales of both storage and computation means thereby opening a gateway to many further innovations and research domains. This increasing boundary of computation also brings along an alarming concern of Cyber Security along with it. Cyber Attacks intended at infrastructures, networks, personal computer devices, targeting information available in the systems and using various tools to change, remove or steal data or information systems are coming into light. One such prominently attempted attack is the SQL Injection Attack. Injection Attack by Structured Query Language (SQL) (IASQL) maintains an attacker's preferred method of stealing confidential data from a database, with potentially disastrous effects, on insecure web applications. Further IASQL can effect in deals with non authorized to use secure data, such as credentials, banking information details, or user information. Most high priorities data has violated in recent days because of query based injection attacks, and it has significant loss and rigid of its action. An attacker can sometimes get a permanent gateway into an organization system, resulting in a long-term penetration that goes unreported for a long time. One approach that would be suggestible to be followed to mitigate SQL Injection attacks is the use of classification to bias safe queries from tweaked queries to detect SQLI attacks. However, IASQL research, there is a lack of predefined, reliable data or collection of data with features and past data items to train a classifier and a majority of detections happen observation in such cases. Through this study, we intend to deploy a web application that makes use of template formats of Injection attacks by SQL by modeling the query symbols and token which has been available to perceive the affected area if a given string or a parameter of a query would end up tampering with the query itself. This would essentially mean that we would define an intelligent system that could detect an anomaly in the query that may make it a threat on production. In this prototype model, we built an application using the technique of Classification and tokenization to propose a new system to detect spiteful websites over the internet. [ABSTRACT FROM AUTHOR]

The utilization of chest X-beam pictures to distinguish the extreme intense respiratory condition COVID 2 (SARS CoV-2), which is answerable for COVID infection 2019 (COVID-19), is life-putting something aside for the two patients and specialists. Moreover, in countries where research facility units for testing are inaccessible, this turns out to be much more basic. The goal of this study was to demonstrate the use of deep learning for high-accuracy COVID-19 identification utilising chest X-ray pictures. The examinations included the preparation of profound learning and AI classifiers utilizing openly accessible X-beam pictures. 38 tests were done with convolutional neural organizations (CNN), ten analyses with five AI models, and fourteen examinations with cutting edge pre-prepared organizations for move learning. To test the exhibition of models, pictures and measurable information were examined autonomously in the preliminaries, and eightfold cross-approval was utilized. The normal affectability is 91.74 percent, the normal explicitness is 97.28 percent, the normal exactness is 97.50 percent, and the normal recipient working qualities region under the bend scores are 94.51 percent. Coronavirus can be identified in a predetermined number of and imbalanced chest X-beam pictures utilizing a convolutional neural organization with insignificant layers and no pre-preparing. [ABSTRACT FROM AUTHOR]

It has been found existing techniques to determine the stress of the soil and processes that are suitable for evaluating a number of variables in a short period of time, and they can react quickly to reveal stress changes in their operation (WSN). "The life span and the work efficiency are inextricably linked." See if you can decrease the network loss by any means other than by using a rational and balanced clusters and interconnecting the clusters. Today's increased demand for WSN has made it more important to consider life cycle lifetime performance, but there has also been an increase in their focus on the original cycle life cycle of the protocol. In the transmission stage, the hybrid mode is used, as well as energies on the previous hops and between the nodes being measured the election of next-hop nodes also has full support. By introducing an uneven clustering mechanism and optimising the competition zone's cluster radius, the simulation shows a significant improvement in data retention and transmission time. [ABSTRACT FROM AUTHOR]