Uniform Resource Locator Research Articles

A distributed dynamic authorisation method for Internet+ medical & healthcare data access based on consortium blockchain

ABSTRACT Privacy protection to medical & healthcare data is one of the bottleneck problems in Internet information fusion. In order to balance data processing capability with privacy concerns, a distributed dynamic authorisation method based on blockchain is proposed to realise trustworthy data access. URLs (Universal Resource Locator) are included in the authorisation information to indicate the data access interface as well as separating patients’ privacy data from blockchain to improve data process efficiency. The contents of smart contracts are demonstrated in a case study to validate the usability of the proposed authorisation method in medical data access between different hospitals.

Detecting Phishing Website Using Machine Learning

A fraud attempt to get sensitive and personal information like password, username, and bank details like credit/debit card details by masking as a reliable organization in electronic communication. The phishing website will appear the same as the legitimate website and directs the user to a page to enter personal details of the user on the fake website. Through machine learning algorithms one can improve the accuracy of the prediction. The proposed method predicts the URL based phishing websites based on features and also gives maximum accuracy. This method uses uniform resource locator (URL) features. We identified features that phishing site URLs contain. The proposed method employs those features for phishing detection. The proposed system predicts the URL based phishing websites with maximum accuracy.

A Comparative Analysis of Search Engine Ranking Algorithms

Ranking Algorithm is the most proper way of positioning on a scale. As the information and knowledge on the internet are increasing every day.The search engine's ability to deliver the most appropriate material to the customer. It is more and more challenging without even any assistance in filtering through all of it. However, searching what user requires is extremely difficult. In this research, an effort has been made to compare and analyze the most popular and effective search engines. The keywords were used in uniform resource locator like, title tag, header, or even the keyword's resembles to the actual text. The page rank algorithm computes a perfect judgment of how relevant a webpage is by analyzing the quality and calculating the number of links connected to it. In this study the keyword relevancy and time response were used for search engines and observed the results. It is observed that the google search engine is faster than the bing and youtube, and after all, bing is the best search engine after google. Moreover, youtube is the fastest search engine in terms of video content search. The google results were found more accurate. However, it is better than all of the search engine

URLdeepDetect: A Deep Learning Approach for Detecting Malicious URLs Using Semantic Vector Models

Malicious Uniform Resource Locators (URLs) embedded in emails or Twitter posts have been used as weapons for luring susceptible Internet users into executing malicious content leading to compromised systems, scams, and a multitude of cyber-attacks. These attacks can potentially might cause damages ranging from fraud to massive data breaches resulting in huge financial losses. This paper proposes a hybrid deep-learning approach named URLdeepDetect for time-of-click URL analysis and classification to detect malicious URLs. URLdeepDetect analyzes semantic and lexical features of a URL by applying various techniques, including semantic vector models and URL encryption to determine a given URL as either malicious or benign. URLdeepDetect uses supervised and unsupervised mechanisms in the form of LSTM (Long Short-Term Memory) and k-means clustering for URL classification. URLdeepDetect achieves accuracy of 98.3% and 99.7% with LSTM and k-means clustering, respectively.

Knowledge about COVID-19 among Citizens in Thi-Qar Governorate/Iraq: an Online Survey

Epidemics of infectious disease, such as coronavirus disease 2019 (COVID-19), necessitates knowledgeassessment of pertinent populations as fast as possible because the findings are of great importance to thepublic health response. This study aims to (i) Assess the knowledge of Iraqi residents about COVID-19 and(ii) Identify the relationship between certain demographic features and the overall knowledge. This studywas conducted in Thi-Qar governorate/Iraq, the researcher used an electronic survey questionnaire (usinggoogle form), the Uniform Resource Locator was distributed through social media platforms with briefintroduction concerning the study. The tool of the study (questionnaire) involved of two parts: demographics(4 items) and knowledge questionnaire (3 sub-domains including 12 items). The total correct answers ratewas (88.1%). Lowest answers rate scored in routes of transmission sub-domain (82.5%) while control andprevention (92.7%) sub-domain scored the highest rate. Mean knowledge score of 10.57 (standard deviation:1.5, Range: 0-12). Also, results display highly significant relationships between overall knowledge anddemographic characteristics (P <0.001).Conclusion: Most well-educated Iraqi residents, particularly males, are knowledgeable about COVID-19.However, this result should be generalized with caution due to limited sample representativeness.

URL Optimization

URL is a technique/process under which we optimize the URL structure of a website/blog page in order to make web page more understandable by the search engine crawlers and by the users. The URL (Universal Resource Locator) is the location of a page on your web site bar. After choosing a domain name, optimizing the URLs for your page is the next SEO step. SEO for your page URLs is important because keywords in the URL are a ranking factor and short and descriptive URLs can help with the link building and user interaction. A URL usually looks something like this: it starts with https:// it is to visit.Hardware Requirement:I3 Processor Based Computer Memory: 8 GB RAM Internet Connection Hard Drive: 10 GB Software Requirement: Windows 7 or higher Technology Used: Nodejs Express database: mongodb. Advantages: User can customize their URLs Track your link performance Valuable analytical tool. Disadvantages: Applications: created URL This is a link management platform that lets you harness the power of your link by shortening, sharing, managing and analysing links to your content.

Robust Detection of Malicious URLs with Self-Paced Wide & Deep Learning

As cybercrimes grow in scale with devastating economic costs, it is important to protect potential victims against diverse attacks. It is the uniform resource locators (URLs) that connect vulnerable users with potential attacks. Although numerous solutions (e.g., rule-based solutions and machine learning-based methods) are proposed for malicious URL detection, they can not provide robust performance due to the diversity of cybercrimes and can not cope with the explosive growth of malicious URLs with the evolution of obfuscation strategies. In this paper, we propose a deep learning-based system, dubbed as CyberLen, to detect malicious URLs robustly and effectively. Specifically, we use factorization machine (FM) to learn the latent interaction among lexical features. For the deep structural features, position embedding is introduced for token vectorization to reduce the ambiguity of URL tokens. Meanwhile, temporal convolution network (TCN) is utilized to learn the long-distance dependency among URL tokens. To fuse heterogeneous features, self-paced wide & deep learning strategy is proposed to train a robust model effectively. The proposed solution is evaluated on a large-scale URL dataset. Our experimental results show that position embedding is constructive to reducing the ambiguity of URL tokens, and the self-paced wide & deep learning strategy shows superior performance in terms of F1 score and convergence speed.

Malicious URL Detection Based on a Parallel Neural Joint Model

A parallel neural joint model algorithm is proposed for the analysis and detection of malicious Uniform Resource Locator (URL). By detecting and analyzing malicious URL's characteristics, the semantic and visual information will be extracted. First, a visualization algorithm is used to realize the visualization of the URL mapping to a gray image with texture characteristics. Second, the lexical feature and character feature of URL are extracted and further processed through word vector technology. These extracted features are transformed into lexical embedding vectors and character embedding vectors. To combine the texture features with text features, a parallel joint neural network combining capsule network (CapsNet) and independent recurrent neural network (IndRNN) is utilized to capture multi-modal vectors of visual and semantic information synchronously. The last layer utilizes the attention mechanism to further filter the deep features extracted from the overall network while concentrating on effective features improving the classification accuracy and analyzing and detect malicious URLs. Based on the experimental results, it is demonstrated that this algorithm has higher accuracy compared to the traditional algorithms.

Detection of Phishing Websites Using Ensemble Machine Learning Approach

In this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrimes in the modern digital sphere where an attacker imitates an existing - and often trusted - person or entity in an attempt to capture a victim’s login credentials, account information, and other sensitive data. Phishing websites are visually and semantically similar to real ones. The rise in online trading activities has resulted in a rise in the number of phishing scams. Cybersecurity jobs are the most difficult to fill, and the development of an automated system for phishing website detection is the need of the hour. Machine Learning is one of the most feasible methods to approach this situation, as it is capable of handling the dynamic nature of phishing techniques, in addition to providing an accurate method of classification.

Development of QR-code based Interactive Dynamic Billboard System with Motion Detection

This paper presents the design and implementation of an intelligent Dynamic electronic billboard based on QR-Code (Quick Response Code) that can be used in a variety of interior locations such as offices, malls, universities, supermarkets, and other similar establishments. A screen display, a sensor such as a PIR motion sensor, and a QR-Code reader are all included in the system. Once scanned, the QR-Code should provide rapid access to information. While the motion sensor detects a nearby person and instructs the system to display the QR-Code on the billboard for the person to scan, the display device continues to display the company's products or announcements. The QR-Code contains Uniform Resource Locators (URLs) in our prototype, and once scanned, a user follows the link and begins exploring what he or she needs to see on a display device based on what the companies sell or communicate with the general public. As a result, the system allows users to interact with it by allowing them to search for what they need using QR-Codes. The Raspberry Pi houses the sensors and display device, allowing the system to relay sensory data to a web server using the HTTP (Hypertext Transfer Protocol) protocol. Because of its low cost, efficiency, and ease of use, the designed system is beneficial.

An Effective Cost-Sensitive XGBoost Method for Malicious URLs Detection in Imbalanced Dataset

Imbalanced class has been a common problem encountered in the modeling process, and has attracted more and more attention from scholars. Biased classifiers, which limit the classifiers' performance for minority classes, will be produced if the imbalanced ratio between the number of positive labels and negative labels is ignored. The synthetic minority over-sampling technique (SMOTE) is a very classic and popular over-sampling method, which is widely used to address this problem. However, SMOTE increases label noise and the training time during the over-sampling process. To improve the detection rate of minority classes while ensuring efficiency, we propose a cost-sensitive XGBoost (CS-XGB) for the imbalanced data problem. The CS-XGB method can reduce the classifiers' preference for most classes without changing the distribution of the original data. 600000 Uniform Resource Locators (URLs) were collected to validate the CS-XGB method. We compare XGBoost (XGB), SMOTE+XGB and CS-XGB, and the experimental results confirm that the CS-XGB is robust and efficient for imbalanced cases.

Specialist Perception of Severe Asthma in Korea: A Questionnaire Survey.

The Working Group on Severe Asthma of the Korean Academy of Allergy and Clinical Immunology recently published an expert opinion paper on the management of severe asthma in Korea. When developing a consensus, the working group encountered several diagnostic and treatment issues and decided to perform a questionnaire survey of Korean specialists with regard to severe asthma. An e-mail with a uniform resource locator link to the questionnaire was sent to 121 asthma specialists, of whom 44.6% responded. The most commonly accepted definitions of severe asthma were a history of fatal exacerbation or an asthma-triggered need for mechanical ventilation, 3–4 oral corticosteroid (OCS) bursts/year, and maintenance of OCS therapy for 3–6 months per year. Before diagnosing severe asthma, most physicians contemplate chest computed tomography, seek to control chronic rhinosinusitis, and consider poor inhaler compliance. For patients with uncontrolled severe asthma accompanied by type 2 (T2)-high inflammation, most biologics available in Korea were considered appropriate, but gaps were apparent in terms of T2-low asthma treatments. These findings about specialist perception of diagnosis and treatment of severe asthma will inform the use of emerging new drugs and facilitate personalized therapy.

The Spatial Analysis of the Malicious Uniform Resource Locators (URLs): 2016 Dataset Case Study

In this study, we aimed to identify spatial clusters of countries with high rates of cyber attacks directed at other countries. The cyber attack dataset was obtained from Canadian Institute for Cybersecurity, with over 110,000 Uniform Resource Locators (URLs), which were classified into one of 5 categories: benign, phishing, malware, spam, or defacement. The disease surveillance software SaTScanTM was used to perform a spatial analysis of the country of origin for each cyber attack. It allowed the identification of spatial and space-time clusters of locations with unusually high counts or rates of cyber attacks. Number of internet users per country obtained from the 2016 CIA World Factbook was used as the population baseline for computing rates and Poisson analysis in SaTScanTM. The clusters were tested for significance with a Monte Carlo study within SaTScanTM, where any cluster with p < 0.05 was designated as a significant cyber attack cluster. Results using the rate of the different types of malicious URL cyber attacks are presented in this paper. This novel approach of studying cyber attacks from a spatial perspective provides an invaluable relative risk assessment for each type of cyber attack that originated from a particular country.

URL normalization and assessing website structures in higher education websites

PurposeThis study aims to explore the character and attainment of an effective URL system by expanding the concept of URL normalization, originally connected to machine-reading access of web pages, to form a broader understanding of URL systematization that includes user-focused cognitive and practical elements.Design/methodology/approachA revised understanding of URL normalization will be used to critically analyzed URLs of main admissions pages from M1 universities, as designated by the Carnegie Foundation.FindingsThe study found that very few institutions implemented well-organized systems of Uniform Resource Locators (URLs) and redirects and that many included unintelligible and impractical URLs that would hinder the effective use of their websites.Practical implicationsA broader understanding of URL systematization will result in more effective website design. URLs must serve an indexical function pointing to a unique web resource, whatever the URL's format. However, URLs should also consider human usability issues and strive to be simple, short, communicable, intelligible and ultimately useful as part of social interactions. Poorly designed URLs create frustration, if not failure, by being difficult to use, confusing or interminable. An effective URL system should also include redirects to anticipate alternate, meaningful URLs that are different from the canonical path. The framework and recommendations arising from this study are applicable to many website structures.Originality/valueThe expanded understanding of the concept of URL normalization and subsequent evaluation principles can be used to assess the overall coherence and completeness of the website in general, thus improving website usability.

Pap Smear Readability on Google: An Analysis of Online Articles Regarding One of the Most Routine Medical Screening Tests

Background: The Papanicolau smear (Pap smear, Pap test) is one of the most routine of all screening tests performed in medicine. Its advent and widespread use has brought a considerable decrease in the incidence of cervical cancer. Unfortunately, this malignancy continues to convey significant morbidity and mortality. Much of this is the result of inadequate compliance with routine Pap smear screening, in which limited education is thought to play a role, particularly among ethnic minorities.
 Methods: A Google search using the phrase “pap smears” was performed and the first fourteen Uniform Resource Locators were analyzed using four standardized readability indices: the Flesh-Kinkaid Grade Level, the SMOG index, the Gunning Fog Index and the Automated Readability Index. An average grade level readability was then obtained and compared to the AMA recommendation that health care information be written at a 5th or 6th grade reading level.
 Results: The average grade level readability of the fourteen analyzed sites using four aforementioned indices were 8.86, 8.82, 11.92 and 8.35, respectfully. The total average readability after considering all four indices was 9.49, three grade levels above what the AMA has recommended.
 Conclusion: The readability of commonly accessed internet information regarding Pap smears is above what is recommended by the AMA. Physicians should be cognizant of this, particularly given this is a routine healthcare test that should be undergone by nearly fifty percent of the world’s population at various points throughout their lifetime.

PNS195 Piloting a Comprehensive Search for Definitions of Digital Health in the Grey Literature: Preliminary Results from a Systematic Scoping Review

A myriad of digital technologies within health systems has emerged over the years and the current pandemic has further catalyzed their development. As a result, various definitions of digital health are often used in different circles. The objective of this work was to provide an overview of definitions available on the internet and their context to complement the traditional (“white”) literature search. We adapted methodological guidelines from the Cochrane Handbook and management sciences to mirror the search in bibliographic databases. A comprehensive Google search was performed to retrieve uniform resource locators (URL’s) of webpages containing terms for digital health within 4 words of synonyms for the word “definition”. The DataScraper extension of the Google Chrome browser was used to collect all URL’s. A preliminary analysis of the definitions for “digital health” was performed. Webpages were eligible if they contained an original or adopted English-language definition of digital health or contained a direct link to a definition or a document containing a definition. All document types were eligible. Of the 38 search expressions built from terms related to digital health, 6 yielded no results and 32 yielded a total of 1928 URL’s. Deduplication resulted in 1840 unique URL’s. The expression containing “digital health” yielded 103 webpages, out of which 37 (35.9%) contained a definition of digital health, and 17 (16.5%) contained definitions of terms related to digital health. The retrieved webpages contained 22 (21.4%) original definitions and 15 (14.6%) adopted or adapted definitions. The digital era raises both challenges and opportunities in conducting a grey literature search. We found that an augmented Google-based search can identify valuable references that traditional literature searches cannot detect. Term definitions (and their context) found in the grey versus bibliographic databases need to be compared to ensure their alignment with ISPOR’s mission.

Classifying and clustering malicious advertisement uniform resource locators using deep learning

AbstractMalicious online advertisement detection has attracted increasing attention in recent years in both academia and industry. The existing advertising blocking systems are vulnerable to the evolution of new attacks and can cause time latency issues by analyzing web content or querying remote servers. This article proposes a lightweight detection system for advertisement Uniform resource locators (URLs) detection, depending only on lexical‐based features. Deep learning algorithms are used for online advertising classification. After optimizing the deep neural network architecture, our proposed approach can achieve satisfactory results with false negative rate as low as 1.31%. We also design a novel unsupervised method for data clustering. With the implementation of AutoEncoder for feature preprocessing and t‐distributed stochastic neighbor embedding for clustering and visualization, our model outperforms other dimensionality reduction algorithms by generating clear clusterings for different URL families.

Evaluating the reliability and readability of online information on osteoporosis.

Internet usage for obtaining health-related information is widely popular among patients. However, there are still concerns about the reliability and comprehensibility of online information. The purpose of this study is to investigate the reliability and readability of osteoporosis-related websites. On April 2, 2020, we searched the term "osteoporosis" on Google (https://www.google.com). We evaluated the first 200 uniform resource locators (URLs) in the query results regarding typology, the Journal of the American Medical Association (JAMA) scores, Health on the Net Foundation Code of conduct (HONcode) certification, Flesch-Kincaid Grade (FKG), and Simple Measure of Gobbledygook (SMOG) scores. The JAMA scoring system and HONcode stamp were used for assessing the reliability, whereas FKG and SMOG scores were used to assess the readability of online information. Of the 151 analyzed websites, 57 (37.7%) were classified as highly reliable, and 19 (12.6%) were assigned with HONcode certification. The average FKG scores (8.81 ± 2.21) and SMOG scores (7.63 ± 1.81) were below the recommended grade, which is considered as easily readable. High reliable information was found to have higher readability scores, thereby representing the difficulty of readability. We observed a weak correlation between the increased reliability of information and decreased readability. Osteoporosis-related content on the internet generally has low reliability. High-reliable information is available online in scientific published materials, health portals, and news. Although the readability of the overall material is acceptable, the high-reliable websites still require high literacy and comprehension skills.

WITHDRAWN: Meta-heuristic approach to enhance the performance of web crawler for web page clustering and link priority evaluation

Abstract The main task of web users is to access the website to search for the necessary data. The search engine is used to access specific page size and giant results. In order to restore, crawl and index the web-pages and maintain the web database update, web crawler uses different web crawling algorithms for the same purpose. For web crawlers a novel web crawling technique called single and multi-threaded web crawling and indexing algorithms is proposed. A URL is divided in crawled pages into two groups, such as inter and intra-domain links. The weight of specific web pages is initialized because the hyperlinks depend on the type and size of the web page connection which is stored in the queue URL. The experimental results indicate that the proposed approach offers better time than current systems. The links are initially stuck from the uniform resource locator using a crawling approach that allows users to hierarchically search their web-links. In order to prevent longer waiting, the user must have a web page in a short time. The time to obtain and implement the proposed approach shows that the efficiency of basic a knowledge search has improved in web page documents.

Cloud-based middleware for supporting batch and stream access over smart healthcare wearable device

In IoT-based smart healthcare services, the heterogeneity of connected wearable sensing devices open up a wide opportunity to develop various healthcare services. However, it also poses an interoperability challenge since each sensing device and application may have different communication mechanisms. Considering that challenge, web platform can be seen as a promising candidate for providing an interoperability layer as we can abstract various devices as single representation i.e. web resource. In this paper, we propose the design of middleware for enabling efficient web of things access over healthcare wearable devices. The proposed middleware consists of three components: gateway-to-cloud device, messaging service and data access interface. The gateway-to-cloud device has a role to perform low level sensor data collection from various wearable sensing device through bluetooth low energy (BLE) communication protocol. Collected data are then relayed to the cloud IoT platform using a lightweight MQTT messaging protocol. In order to provide device abstraction along with access to the stored data, the system offers two kind of interfaces: the Restful HTTP identified by unique universal resource locator (URL) for batch access and MQTT websocket interface identified by unique topic to accommodate access on sensing data in near real time stream manner.