Single Click Research Articles

ONLINE RADICALIZATION: TWITTER PRIVATIZATION AS A THREAT TO THE MODERN SOCIETY

Social media plays a key role in the contemporary world, having indefinite power to influence people by just a single post and click of a button, reaching millions of people in a few seconds. In April 2022, Elon Musk announced his step into the world-famous‘wall of opinions’ – Twitter– with words of creating a better, free-speech-based platform with no limitations. Since online radicalization became the easiest and most powerful recruiting tool for terrorist and extremist organizations, this raised concerns about whether this action is beneficial or not. The aim of the paper was to describe the principle of online radicalization and present it via evidence-based examples, as well as transpose the known aspects of Elon Musk’s purchase of Twitter. A comparison study was made between these known examples of radicalization on the Internet, currently used preventive policy principles and possible future steps based on the announcements of Elon Musk himself. Results confirmed that online radicalization is the most effective method for extremists, and that the society and authorities should keep an eye on the development of the platform regarding the real threat of losing control over the content that can be harmful to Twitter users. Free speech is a strong argument for freeing the rules of sharing content, but the ‘dark side’ of this move has to be considered as well.

Automate the Deployment of Any Custom Web Applications Across Several Clouds using Kubernetes Clusters

During COVID period, most the businesses are worked in online mode. For deploying their businesses need to keep their web apps up all throughout the clock. Every successful small-scale business has its own website or [3 ]web application. To deploy the website with every modification needed, a team of DevOps Engineers is needed. The role of those engineers is to take the website, run it on a server and make it available to the end-user. This entire process can therefore be automated in a single click with this system. In this system, we are integrating the DevOps tools such as Ansible, Terraform, Kubernetes, Docker and Cloud Servers. This system when used will save more time and would be much more efficient. It will help businesses cost-effectively achieve zero downtime. Along with these benefits, their website or web app would be secured as it would be deployed and monitored on Cloud. This system will help businesses to grow more.

Memory Load and Performance-based Adaptive Smartphone E-learning Framework for E-commerce Applications in Online Learning

<p>The term e-commerce is not confined to the purchase and sale of goods only. There are several occasions where students are not able to comprehend any idea on their own. With the availability of online learning platforms such as Massive Open Online Courses (MOOCs) and Virtual Learning Environments (VLEs), students may visit online Internet sites, where various articles are made available with a single click. With mobile devices in their hands, they can also listen to the presentations of different academics from anywhere and at any time. In an online learning environment, it is challenging to provide tailored learning content to students that fulfill their needs and requirement. Therefore, we in this research paper propose an adaptive smartphone learning framework. The framework first considers students’ academic performance, preferred learning content, and memory load i.e., cognitive characteristics. Based on students’ academic performance, preferred learning content, and memory load, appropriate adaptive and motivational triggers are sent on students’ smartphones to guide and motivate them in their learning process. We have introduced ten types of adaptive triggers that in different scenarios will be presented to students to help them in remembering important learning points. Furthermore, performance-based motivational triggers in connection with adaptive triggers are also introduced. These motivational triggers are sent on students’ smartphones for giving them a suggestion, hope, appreciation, and warnings during their studies. In this study, we also show how different students’ learning scenarios are created and how our framework deals with those scenarios in students’ learning process.</p> <p> </p>

N-Cubic q-Rung Orthopair Fuzzy Sets: Analysis of the Use of Mobile App in the Education Sector.

This study analyzes the description to examine the results of a new study and create the technique and also demonstrate the effectiveness of this technique. In this ever-changing world, students are increasingly encouraged to use mobile phones primarily to learn for educational purposes. The learning process is continuous and the goal has now been achieved. It has been replaced by online learning. Due to mobile phones as well as the many feature-oriented applications, students can study at their own place and use the application to spend their time understanding, because everything is accessible with a single click. To carry on the study we applied mobile applications for online education system. Now, because the traditional method is taken into consideration, it is normal to carry a bag full of books and copies and immerse yourself in the tradition of learning to write. However, it has been found that not all students learn when he takes notes. Therefore, we must make sure that the student focuses only on one thing at a time. To continue the research, we apply the N-cubic structure to q-rung orthopair fuzzy sets in multi-attribute group decision-making problems. This structure solves the problems of multi-attribute group decision-making techniques more generally.

Customer Satisfaction: An Insight Towards E-Commerce

Today the world is becoming digital in every part. The internet-services are charming with ‘popularity’ (fame) and the vital facet for the communication growth technologies are creating (making) a very fast improvement. Every year National Technology Day is celebrated on May 11 and this year theme is 'An Integrated Approach to Science and Technology for a Sustainable Future'. As Technology is substituting the phase of traditional methods of buying and selling in terms of online ecommerce. Now-a-days folks choose to purchase or shop the products (goods) by just 1 click (one single click), by via gadgets. E--commerce firms provide extreme service quality to the users or customers, and also preserve trust of users (customers) and satisfaction. Website value is ‘central-objective’ for acquiring customer satisfaction and e-commerce website triumph. Based on TAM (total addressable market) model, let us analyse the effect of customer satisfaction. Website quality may consist of three dimensions: system quality, service quality and information quality.

Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using Jupyter notebooks

Modern biomedical image analyses workflows contain multiple computational processing tasks giving rise to problems in reproducibility. In addition, image datasets can span both spatial and temporal dimensions, with additional channels for fluorescence and other data, resulting in datasets that are too large to be processed locally on a laptop. For omics analyses, software containers have been shown to enhance reproducibility, facilitate installation and provide access to scalable computational resources on the cloud. However, most image analyses contain steps that are graphical and interactive, features that are not supported by most omics execution engines. We present the containerized and cloud-enabled Biodepot-workflow-builder platform that supports graphics from software containers and has been extended for image analyses. We demonstrate the potential of our modular approach with multi-step workflows that incorporate the popular and open-source Fiji suite for image processing. One of our examples integrates fully interactive ImageJ macros with Jupyter notebooks. Our second example illustrates how the complicated cloud setup of an computationally intensive process such as stitching 3D digital pathology datasets using BigStitcher can be automated and simplified. In both examples, users can leverage a form-based graphical interface to execute multi-step workflows with a single click, using the provided sample data and preset input parameters. Alternatively, users can interactively modify the image processing steps in the workflow, apply the workflows to their own data, change the input parameters and macros. By providing interactive graphics support to software containers, our modular platform supports reproducible image analysis workflows, simplified access to cloud resources for analysis of large datasets, and integration across different applications such as Jupyter.

Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease.

Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but training data is limited, as when segmenting cardiac structures in patients with congenital heart disease (CHD). In this paper, we propose an iterative segmentation model and show that it can be accurately learned from a small dataset. Implemented as a recurrent neural network, the model evolves a segmentation over multiple steps, from a single user click until reaching an automatically determined stopping point. We develop a novel loss function that evaluates the entire sequence of output segmentations, and use it to learn model parameters. Segmentations evolve predictably according to growth dynamics encapsulated by training data, which consists of images, partially completed segmentations, and the recommended next step. The user can easily refine the final segmentation by examining those that are earlier or later in the output sequence. Using a dataset of 3D cardiac MR scans from patients with a wide range of CHD types, we show that our iterative model offers better generalization to patients with the most severe heart malformations.

User Interface and AI Navigation System for Autonomous Vehicle

The issue of vehicle accidents due to human error and behavior (rash driving, drink and drive, etc.) is on rising resulting into fatality and economical loss. In order to overcome these issues, the autonomous vehicle is being explored as a probable solution. Driver will be replaced by AI based autonomous system; The main objective is to convert the manual operated EV into autonomous vehicle. The project work is divided into three main parts i.e., Control System for Vehicle Control, AI (Artificial Intelligence) and UI (User Interface). The main objective of User Interface is to give information regarding vehicles health i.e. battery remaining, charging time, distance to be covered, etc. AI will be use as decision making system or we can say brain of the autonomous vehicle. System Control or Vehicle Control System is to control the vehicle’s steering, acceleration and braking system with the help of PID controller while the vehicle is being operated in autonomous mode. The main purpose is to make an autonomous EV for the passengers to go to their selected destination with a single click by selecting destination on display (UI). So the vehicle control is responsible for the vehicle’s steering i.e., at what desired angle the vehicle need to turn, during acceleration the factors that are considered are; at what speed the vehicle needs to move in an straight path or during a turn and the braking system is operated with the help of LIDAR where if it detects an object at what distance it needs to apply the brakes and when to release the brakes. All the factors and conditions required in developing the autonomous vehicle will be considered, as use cases to improve the accuracy of autonomous vehicle, resulting into achieving desired objective of safe travel.

Data Analysis and Visualization of Sales Dataset using Power BI

Abstract: Power BI has completely revolutionized the worlds of business intelligence, data visualization, and analytics. Power BI is an online service that allows users to search for data, transform it, visualize it, and share the reports and dashboards they create with other users in the same or different departments/organizations, as well as the general public. Power BI is used by over 200,000 organizations in 205 countries as of February 2017. Power BI's Quick Insights feature is an innovative feature that is built on a growing set of advanced analytical algorithms. This function may be activated with a single click after uploading a data set to Power BI, and it generates a number of reports depending on the data's analysis without any need for human participation. This also aids in reducing human errors in calculations and statistical techniques, which can lead to research that isn't verifiable. Power BI is simple to use and ready for adoption as a platform for Research Data Analysis, accepting even Excel spreadsheets as input. The pandemic of the coronavirus has hastened the adoption of mobile-friendly analytics and business intelligence platforms. Companies all over the world now want their employees and customers to have access to data and analytics from any location. The mobile BI market will be worth more than $20 billion by 2024, according to Mordor Intelligence. Furthermore, mobile devices such as smartphones and tablets are expected to account for more than 72 percent of all internet traffic. As a result, implementing mobile-friendly BI platforms across your organization in 2022 makes even more sense from a business perspective. The purpose of this paper is to show how Power BI can quickly transform a piece of research data into a set of shareable analytical reports and dashboards.

Seroprevalence Survey of Brucella Infections in Poultry in Selected Local Government Areas of Yobe State, Nigeria

This study determined the seroprevalence, epidemiological factors and geospatial distribution of Brucella infection in indigenous breed of chicken in Yobe State, Nigeria. Random sampling was used in selecting the locations. Blood for serum were collected from 400 chickens in two Live Bird Markets and tested using mRBPT and SAT (EDTA). Coordinates of these markets were taken by a single click using a handheld GPS machine. Seroprevalence of 21.0% (mRBPT) and 18.0% (SAT-EDTA) from mRBPT positive samples were obtained which were not statistically significant based on locations (p>0.05). Seroprevalence by sex was 19.5% and 88.4% for male chickens using mRBPT and SAT-EDTA respectively and 21.8% and 84.6% for female chickens using mRBPT and SAT-EDTA respectively. There was no statistically significant difference in seropositivity by sex and test type (p>0.05). Adult chickens had seroprevalence of 20.5% and 84.5% using mRBPT and SAT-EDTA respectively while 20.8% and 100.0% were obtained in the young using mRBPT and SAT-EDTA respectively. There was no statistically significant difference in the seroprevalence by age (p>0.05). Geospatial map showed a distribution between 0.8% to 3.3% in the sampled areas. The study demonstrated infection by Brucella species in indigenous breed of chickens of both sexes and ages in Yobe state using mRBPT and SAT-EDTA. There is the need to create more awareness for stakeholders in the poultry industry in the State on the mode of transmission and prevention of the disease in order to reduce the menace that can be caused by the disease. Key words: Seroprevalence, Brucella infection, epidemiological factors, geo-spatial mapping

Advanced Reverse Image Search and Profile Creation using Machine Learning

The Reverse image search is most widely used image search engine due to its extensive database that contains billions of images uploaded over the web. It is best to use the image search when your aim is to find identical images against your queried image. The reverse image search by image is idle option for people getting down similar images in different quality, size, or formats. This online facility leads us to explore image search results with a single click. A convolutional neural network is a type of artificial neural network used for image processing.

Root Finding Methods Through GUI in Spreadsheets

Root finding Methods like the Bisection Method, Newton Raphson Method, Secant Method, and False Position Method have been revisited through a new approach. EACH METHOD’S user-friendly GUI computer programs have been developed on an Excel spreadsheet. A root locator graph is introduced in the spreadsheets, which helps identify the initial guess(es) required to calculate root using these methods. All real roots are now easily calculable by any of the four methods. The detailed steps to calculate roots are also shown on the spreadsheet. The spreadsheet can be made ready for the next calculation with a single click. We have developed a technique for Excel that helps accept polynomial in variable ‘x’ without directly mentioning cell location.

The Cancer Research Database (CRDB): Integrated Platform to Gain Statistical Insight Into the Correlation Between Cancer and COVID-19

BackgroundThe advancement of cancer research has been facilitated through freely available cancer literature, databases, and tools. The age of genomics and big data has given rise to the need for cooperation and data sharing in order to make efficient use of this new information in the COVID-19 pandemic. Although there are many databases for cancer research, their access is not easy owing to different ways of processing and managing the data. There is an absence of a unified platform to manage all of them in a transparent and more comprehensible way.ObjectiveIn this study, an improved integrated cancer research database and platform is provided to facilitate a deeper statistical insight into the correlation between cancer and the COVID-19 pandemic, unifying the collection of almost all previous published cancer databases and defining a model web database for cancer research, and scoring databases on the basis of the variety types of cancer, sample size, completeness of omics results, and user interface.MethodsDatabases examined and integrated include the Data Portal database, Genomic database, Proteomic database, Expression database, Gene database, and Mutation database; and it is expected that this launch will sort, save, advance the understanding and encourage the use of these resources in the cancer research environment.ResultsTo make it easy to search valuable information, 85 cancer databases are provided in the form of a table, and a database of databases named the Cancer Research Database (CRDB) has been built and presented herein. Furthermore, the CRDB has been herein equipped with unique navigation tools in order to be explored by three methods; that is, any single database can be browsed by typing the name in the given search bar, while all categories can be browsed by clicking on the name of the category or image expression icon, thus serving as a facility that could provide all the category databases on a single click.ConclusionsThe computational platform (PHP, HTML, CSS, and MySQL) used to build CRDB for the cancer scientific community can be freely investigated and browsed on the internet and is planned to be updated in a timely manner. In addition, based on the proposed platform, the status and diagnoses statistics of cancer during the COVID-19 pandemic have been thoroughly investigated herein using CRDB, thus providing an easy-to-manage, understandable framework that mines knowledge for future researchers.

RNAsolo: a repository of cleaned PDB-derived RNA 3D structures.

MotivationThe development of algorithms dedicated to RNA three-dimensional (3D) structures contributes to the demand for training, testing and benchmarking data. A reliable source of such data derived from computational prediction is the RNA-Puzzles repository. In contrast, the largest resource with experimentally determined structures is the Protein Data Bank. However, files in this archive often contain other molecular data in addition to the RNA structure itself, which—to be used by RNA processing algorithms—should be removed.ResultsRNAsolo is a self-updating database dedicated to RNA bioinformatics. It systematically collects experimentally determined RNA 3D structures stored in the PDB, cleans them from non-RNA chains, and groups them into equivalence classes. It allows users to download various subsets of data—clustered by resolution, source, data format, etc.—for further processing and analysis with a single click.Availability and implementationThe repository is publicly available at https://rnasolo.cs.put.poznan.pl.

Clinician Responses to a Clinical Decision Support Advisory for High Risk of Torsades de Pointes.

BackgroundTorsade de pointes (TdP) is a potentially fatal cardiac arrhythmia that is often drug induced. Clinical decision support (CDS) may help minimize TdP risk by guiding decision making in patients at risk. CDS has been shown to decrease prescribing of high‐risk medications in patients at risk of TdP, but alerts are often ignored. Other risk‐management options can potentially be incorporated in TdP risk CDS. Our goal was to evaluate actions clinicians take in response to a CDS advisory that uses a modified Tisdale QT risk score and presents management options that are easily selected (eg, single click).Methods and ResultsWe implemented an inpatient TdP risk advisory systemwide across a large health care system comprising 30 hospitals. This CDS was programmed to appear when prescribers attempted ordering medications with a known risk of TdP in a patient with a QT risk score ≥12. The CDS displayed patient‐specific information and offered relevant management options including canceling offending medications and ordering electrolyte replacement protocols or ECGs. We retrospectively studied the actions clinicians took within the advisory and separated by drug class. During an 8‐month period, 7794 TdP risk advisories were issued. Antibiotics were the most frequent trigger of the advisory (n=2578, 33.1%). At least 1 action was taken within the advisory window for 2700 (34.6%) of the advisories. The most frequent action taken was ordering an ECG (n=1584, 20.3%). Incoming medication orders were canceled in 793 (10.2%) of the advisories. The frequency of each action taken varied by drug class (P<0.05 for all actions).ConclusionsA modified Tisdale QT risk score–based CDS that offered relevant single‐click management options yielded a high action/response rate. Actions taken by clinicians varied depending on the class of the medication that evoked the TdP risk advisory, but the most frequent was ordering an ECG.

Implementing robotic process automation to improve operational efficiency in the health-care sector

Robotic Process Automation (RPA) is a technique that is rapidly being utilised as a tool to automate, maintain, analyse, and update existing databases in a cost-effective and error-free manner. The majority of healthcare procedures are the same. As a result, deploying Automation solutions in the healthcare industry pays off handsomely for the firm. The Patient Registration procedure is one of the best application reasons for RPA adoption. As requested by the hospital, collect information from patients. Finally, all of the patient's records should be integrated and updated in one location. Manually performing these activities takes a long time and can result in a lot of human mi stakes. Patients must also wait in line to submit their applications if they are done manually. RPA in Patient Register will not only speed up and streamline the process, but it will also improve customer satisfaction and provide a competitive advantage. As a result, a healthcare bot can take over such routine activities from a human staff, allowing them to focus on more important diagnostic and analytical tasks. In addition, an RPA database may be utilised to schedule doctor's visits and deliver all pertinent information to the doctor with a single click.

Car Service Slot Booking System

Abstract: The purpose of implementing this application was for users to book a slot for car servicing by using the car bath app. The automobile Car Bath industry is additionally growing staggeringly and automobile owners favor requesting cleanup services through their mobile devices. To make the automotive wash programming computer code additional reliable, distinctive panels are developed for all users. The service provider's panel advantages to showcasing and automatizing the entire automotive particularization and washing services business online are viable in a very single click. The options of our appointment booking computer code have created the net automotive wash system sturdy. easy showing the services, booking the appointments 24/7, the choice of fixing the service packages, on-line transactions, etc. area unit a number of the main characteristics to spice up productivity. This application is simple and users will book the automotive service squeeze the automotive Car Bath app. This application mostly saves time and human work free. Users can book the services at anytime and anywhere. This type of service is now growing very rapidly and the booking system is used very easily and more accessible. Now every ware there is all online services and just one click away. The car Bath app is allowed to book the slot for car service. By using this app customers can book the service at their flexible time. It's a user-friendly application. In this application customer, data will store very securely. By using this application customers can save time and energy. By using this app, we can book any type of vehicle for service. Currently, technology is very fast. People want to measure an expensive life with minimum physical work. This application is an android app that will be run on any android compatible tablet and portable. The app can alter any automobile user to book an appointment for automobile service. Index Terms: Car bath service, android application, booking, and user-friendly.

COVID-19 and Virtual Medical Student Education.

COVID-19 and Virtual Medical Student Education.

Context-Based Fake News Detection Model Relying on Deep Learning Models

Nowadays, due to the great accessibility to the internet, people seek out and consume news via social media due to its low cost, ease of access, and quick transmission of information. The tremendous leverage of social media applications in daily life makes them significant information sources. Users can post and share different types of information in all their forms with a single click. However, the cost becomes expensive and dangerous when non-experts say anything about anything. Fake news are rapidly dominating the dissemination of disinformation by distorting people’s views or knowledge to influence their awareness and decision-making. Therefore, we have to identify and prevent the problematic effects of falsified information as soon as possible. In this paper, we conducted three experiments with machine learning classifiers, deep learning models, and transformers. In all experiments, we relied on word embedding to extract contextual features from articles. Our experimental results showed that deep learning models outperformed machine learning classifiers and the BERT transformer in terms of accuracy. Moreover, results showed almost the same accuracy between the LSTM and GRU models. We showed that by combining an augmented linguistic feature set with machine or deep learning models, we can, with high accuracy, identify fake news.

A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics

ObjectivesTo determine the value of a deep learning masked (DLM) auto-fixed volume of interest (VOI) segmentation method as an alternative to manual segmentation for radiomics-based diagnosis of clinically significant (CS) prostate cancer (PCa) on biparametric magnetic resonance imaging (bpMRI).Materials and methodsThis study included a retrospective multi-center dataset of 524 PCa lesions (of which 204 are CS PCa) on bpMRI. All lesions were both semi-automatically segmented with a DLM auto-fixed VOI method (averaging < 10 s per lesion) and manually segmented by an expert uroradiologist (averaging 5 min per lesion). The DLM auto-fixed VOI method uses a spherical VOI (with its center at the location of the lowest apparent diffusion coefficient of the prostate lesion as indicated with a single mouse click) from which non-prostate voxels are removed using a deep learning–based prostate segmentation algorithm. Thirteen different DLM auto-fixed VOI diameters (ranging from 6 to 30 mm) were explored. Extracted radiomics data were split into training and test sets (4:1 ratio). Performance was assessed with receiver operating characteristic (ROC) analysis.ResultsIn the test set, the area under the ROC curve (AUCs) of the DLM auto-fixed VOI method with a VOI diameter of 18 mm (0.76 [95% CI: 0.66–0.85]) was significantly higher (p = 0.0198) than that of the manual segmentation method (0.62 [95% CI: 0.52–0.73]).ConclusionsA DLM auto-fixed VOI segmentation can provide a potentially more accurate radiomics diagnosis of CS PCa than expert manual segmentation while also reducing expert time investment by more than 97%.Key Points• Compared to traditional expert-based segmentation, a deep learning mask (DLM) auto-fixed VOI placement is more accurate at detecting CS PCa.• Compared to traditional expert-based segmentation, a DLM auto-fixed VOI placement is faster and can result in a 97% time reduction.• Applying deep learning to an auto-fixed VOI radiomics approach can be valuable.