Real-time Web Research Articles

Web reference decay: Passing the HTTP errors instead of quality information

Web resource links routinely fail, and the content of links might change suddenly and without notice. When referrals to online sites offer proof or supporting data, these web dynamics are harmful. Any hyperlink’s final content is susceptible to two phenomena: the link breaks (link rot) and the content changes from its original state (content drift). The combination of both effects is referred to as reference rot. This paper aims to determine the application of online citations, the number of lost web citations, and the process of recovering missing web citations through the application of the Internet Archive’s Wayback Machine. According to the analysis, the Webology journal published 428 research articles between 2004 and 2020 that had 2,273 online citations. There are 1,260 (55.43%) accessible web citations out of the mentioned ones, whereas 1,013 (44.57%) are missing. There are 1,856 (81.65%) active web citations, up from 1,260 (55.43%) after an effort was made to retrieve 596 lost web citations using the Wayback Machine tool. That meant that the overall increase of the active URLs was 26.22%. The article presents a method for creating specific collections for online archiving in the future, even though web-archiving tools now take snapshots of websites in real time. The paper recommends that future curation standards for real-time web archiving shall include platform dynamics and cultural variations in link-sharing habits.

Empowering Diversity in Education: A Web-Based Tool for Real-Time Sign Language Detection

People worldwide have severe hearing loss and there is little to no technological involvement to empower diversity and inclusion in the education system. This study aims to provide an understanding of the needs of the community of such individuals and a possible solution to the problem using current advances in artificial intelligence technologies. In particular, we will present a solution to the problem through a real-time sign language detection web application. First, the structural and functional aspects of the application are described, followed by the software architecture needs. The technological aspect of the application is entirely defined by expressing all the significant implementations of each programming language contained behind the software diagrams and architecture. This study will provide the latest technologies in the market combined with a well-planned and developed image detection application that is scalable, portable, maintainable, and efficient in empowering diversity in education. This technologically up-to-date application is delivered as a full-stack web application. The application produces results that waver between 85%-95% quality off detection; it consists of a bi-partite backend, one for creating and compiling models, namely Python, TensorFlow, and Jupyter Notebooks, and the other part held by Node.JS to power the front-end built with React and TensorFlow's JS. This study presents a fully developed web-based application that has the potential to impact and improve diversity in education significantly.

Model for converting data into NoSQL data warehouse for developing a real-time financial data warehouse system

This research introduces a novel model, the Financial Data Warehouses API (FDW-API), developed using PHP, Node.js, and Express.js. The model is designed to transform banking credit dataset information into a data warehouse format using a Non-Only SQL (NoSQL) database, stored in JSON format. Three types of databases were employed: MongoDB Node, MongoDB Serverless, and Cassandra. The study includes a comparative analysis of the data retrieval speed from all three databases. The model's applicability was tested in a real-time credit approval web application, demonstrating its effectiveness in transforming and storing data. Testing involved loading datasets ranging from 200, 300, 400, 500, 600, 800, and 1000 entries. Results indicate that the MongoDB serverless database outperformed others in terms of efficiency. Additionally, the FDW-API model streamlines data transformation and storage, facilitating real-time analysis and decision-making for financial institutions and data-driven businesses. Its flexibility integrates seamlessly with existing systems, reducing development time and costs, while its scalability accommodates growing data volumes and evolving business needs, providing a valuable tool for strategic insights and competitive advantage.

An efficient starling murmuration-based secure web service model for smart city application using DBN

The advent of IoT devices increased internet usage more than smartphones and personal computers. The manual analysis of the Web Service Description Language (WSDL) document is quite expensive and time-consuming, hence this paper proposes a novel deep learning architecture to overcome the issue associated with web service classification. A structural self-organized deep belief network (SSODBN) is used for real-time web service classification in different fields such as Education, Smart electricity, Intelligent road networks, Health and social care, and Sports, water, and gas distribution. The SSODBN architecture utilizes a dropout strategy to minimize the interrelationship between the feature detectors and a regularized reinforced transfer function to eliminate the irrelevant weights. The main advantage offered by the S-DBN architecture is the improved preprocessing with feature selection. The Starling Murmuration Optimizer (SMO) is utilized in this paper to minimize the reconstruction error of the S-DBN architecture. The security of the smart city architecture is mainly improved via the blockchain defined network (BDN) using user-authenticated blocks. The experimental results revealed that the proposed method managed the scalability, latency, and centralization issues with superior data transmission.

AI Model for Industry Classification Based on Website Data

This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model’s learning efficacy. Subsequent validation on a separate dataset revealed the model’s robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company’s product portfolio. Based on the model’s performance and its characteristics, we believe that the process of relative valuation can be drastically improved.

Learning to Sketch: A Neural Approach to Item Frequency Estimation in Streaming Data.

Recently, there has been a trend of designing neural data structures to go beyond handcrafted data structures by leveraging patterns of data distributions for better accuracy and adaptivity. Sketches are widely used data structures in real-time web analysis, network monitoring, and self-driving to estimate item frequencies of data streams within limited space. However, existing sketches have not fully exploited the patterns of the data stream distributions, making it challenging to tightly couple them with neural networks that excel at memorizing pattern information. Starting from the premise, we envision a pure neural data structure as a base sketch, which we term the meta-sketch, to reinvent the base structure of conventional sketches. The meta-sketch learns basic sketching abilities from meta-tasks constituted with synthetic datasets following Zipf distributions in the pre-training phase and can be quickly adapted to real (skewed) distributions in the adaption phase. The meta-sketch not only surpasses its competitors in sketching conventional data streams but also holds good potential in supporting more complex streaming data, such as multimedia and graph stream scenarios. Extensive experiments demonstrate the superiority of the meta-sketch and offer insights into its working mechanism.

Enhancement of E-Banking System in Iraq by web application-based authentication system using face recognition

Enabling customers to access their bank accounts electronically brings many benefits, facilitating interaction between customers and banks, and saving time and effort. Customers no longer need to visit physical bank branches or exchange manual transactions. This transformation can be achieved through a strong electronic authentication system that allows customers to access their bank accounts without visiting a bank branch. The foundation of this system is a powerful and unique biometric authentication feature – facial recognition. Have developed a web application that uses real-time facial recognition for electronic banking systems. Iraqi banks previously lacked such advanced methods of electronic authentication, and research seeks to fill this gap. Implementing this system increases customer confidence regarding the security of their accounts. By using a real-time web application that processes real data, the system can accurately identify and tag individuals, and grant or deny them access as appropriate. Two web applications were created, the first using Olivette's global database for image matching and recognition. The second real-time application allows users to access their bank accounts by taking instant photos of them, and detecting, processing, and identifying the people in the photos. If an unauthorized person gains access, access will be denied. Using the Principal Component Analysis (PCA) algorithm in conjunction with the Eigenface technique and the Support Vector Machine (SVM) classifier, it is possible to achieve high accuracy in face recognition in both web programs. Specifically, able to achieve an accuracy of up to 92% and then improve it to 94%.

Retracted: Research on English Online Test Platform Based on Real-Time Web Communication Technology

Retracted: Research on English Online Test Platform Based on Real-Time Web Communication Technology

Enhancing Neonatal Incubator Energy Management and Monitoring through IoT-Enabled CNN-LSTM Combination Predictive Model

This research focuses on enhancing neonatal care by developing a comprehensive monitoring and control system and an efficient model for predicting electrical energy consumption in incubators, aiming to mitigate potential adverse effects caused by excessive energy usage. Employing a combination of 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) methods within the framework of the Internet of Things (IoT), the study encompasses multiple components, including hardware, network, database, data analysis, and software. The research outcomes encompass a real-time web application for monitoring and control, temperature distribution visualizations within the incubator, a prototype incubator, and a predictive energy consumption model. Testing the LSTM method resulted in an RMSE of 42.650 and an MAE of 33.575, while the CNN method exhibited an RMSE of 37.675 and an MAE of 30.082. Combining CNN and LSTM yielded an RMSE of 32.436 and an MAE of 25.382, demonstrating the potential for significantly improving neonatal care.

HerbaVisionNet: Optimized CNN And Resnet50v2 Model For Enhanced Medicinal Plant Identification And Application Design

Addressing the escalating demand for medicinal plants, this study undertakes the critical task of establishing an efficient identification system. Employing Convolutional Neural Networks (CNNs) with ResNet50v2 architecture, the project endeavours to develop a robust model capable of classifying 30 distinct medicinal plant types from high-resolution images. This classification relies on the precision of feature extraction to ensure accurate identification. Notably, the dataset used is meticulously curated to ensure its adaptability to the diverse botanical characteristics inherent in medicinal plants. Central to achieving high accuracy is the ResNet50v2 model, which forms the cornerstone of the project. Integrated into a real-time web application implemented with Flask, this model facilitates swift and accessible plant identification for various users, including researchers, herbalists, and enthusiasts. The study underscores the efficacy of deep learning algorithms in medicinal plant identification, showcasing remarkable accuracy and reliability in classification tasks. Beyond mere identification, the model demonstrates an ability to precisely categorize plant species, thereby bridging the gap between identification and exploration. This precision is underscored by an exceptional 99% accuracy rate in distinguishing between 30 medicinal plant species, attributed to meticulous model training with a diverse, high-resolution dataset capturing intricate plant features. Continual efforts are directed towards expanding the dataset to encompass variations in real-world scenarios. Strategies to address class imbalance through oversampling techniques and class weight adjustments are being rigorously pursued. Additionally, the model undergoes refinement through fine-tuning its architecture and integrating advanced data augmentation techniques. The research significantly contributes to the broader field of AI applications in sustainable practices, particularly in the conservation of medicinal plant resources. Serving as a potent tool for accurate identification, it holds promising implications for future advancements in deep learning and botanical research.

Meta-Sketch: A Neural Data Structure for Estimating Item Frequencies of Data Streams

To estimate item frequencies of data streams with limited space, sketches are widely used in real applications, including real-time web analytics, network monitoring, and self-driving. Sketches can be viewed as a model which maps the identifier of a stream item to the corresponding frequency domain. Starting from the premise, we envision a neural data structure, which we term the meta-sketch, to go beyond the basic structure of conventional sketches. The meta-sketch learns basic sketching abilities from meta-tasks constituted with synthetic datasets following Zipf distributions in the pre-training phase, and can be fast adapted to real (skewed) distributions in the adaption phase. Extensive experiments demonstrate the performance gains of the meta-sketch and offer insights into our proposals.

BRANCACCI CHAPEL IN FLORENCE: SURVEYING AND REAL-TIME 3D SIMULATION FOR CONSERVATION AND COMMUNICATION PURPOSES

Abstract. In 2020 to diagnose and monitor the condition of the frescoes in the Brancacci Chapel in Florence, the CNR initiated an important diagnostic and three-dimensional survey of the monument. These restoration preparatory activities have provided the opportunity and data for the creation of a real-time 3D simulation model that will not only help conservators and art historians better understand and manage the artwork, but will also enable the planning of improvement measures and the communication of the fresco cycle and its state of preservation to the public via the Brancacci POV application. Brancacci POV is a hybrid multi-user experience that allows a group of tourists to visit the chapel under the guidance of an expert, either on-site or off-site. The Web3D application is based on ATON, a framework developed by CNR ISPC that integrates 3D simulation of the monument and the discovery of invisible layers (e.g., ultraviolet data) processed and optimised for real-time rendering. The article illustrates all the steps performed from the 3D survey to the Web3D implementation to obtain the 3D simulation and achieve the expected interactive result. In particular, we focus on the optimization workflow of the reality-based model developed for this case study and on the processing of high-resolution maps (visible and invisible layers) for real-time web interaction.

An efficient feature selection and classification approach for an intrusion detection system using Optimal Neural Network

Network flaws are used by hackers to get access to private systems and data. This data and system access may be extremely destructive with losses. Therefore, this network intrusions detection is utmost significance. While investigating every feature set in the network, deep learning-based algorithms require certain inputs. That’s why, an Adaptive Artificial Neural Network Optimized with Oppositional Crow Search Algorithm is proposed for network intrusions detection (IDS-AANN-OCSA). The proposed method includes several phases, including feature selection, preprocessing, data acquisition, and classification. Here, the datas are gathered via CICIDS 2017 dataset. The datas are fed to pre-processing. During pre-processing, redundancy eradication and missing value replacement is carried out with the help of random forest along Local least squares for removing uncertainties. The pre-processed datas are fed to feature selection to select better features. The feature selection is accomplished under hybrid genetic algorithm together with particle swarm optimization technique (GPSO). The selected features are fed to adaptive artificial neural network (AANN) for categorization which categorizes the data as BENIGN, DOS Hulk, PortScan, DDoS, DoS Golden Eye. Finally, the hyper parameter of adaptive artificial neural network is tuned with Oppositional Crow Search Algorithm (OCSA) helps to gain better classification of network intrusions. The proposed approach is activated in Python, and its efficiency is evaluated with certain performance metrics, like accuracy, recall, specificity, precision, F score, sensitivity. The performance of proposed approach achieves better accuracy 99.75%, 97.85%, 95.13%, 98.79, better sensitivity 96.34%, 91.23%, 89.12%, 87.25%, compared with existing methods, like One-Dimensional Convolutional Neural Network Based Deep Learning for Network Intrusion Detection (IDS-CNN-GPSO), An innovative network intrusion detection scheme (IDS-CNN-LSTM) and Application of deep learning to real-time Web intrusion detection (IDS-CNN-ML-AIDS) methods respectively.

Multichannel CNN-BLSTM Architecture for Speech Emotion Recognition System by Fusion of Magnitude and Phase Spectral Features Using DCCA for Consumer Applications

Conventional Speech Emotion Recognition (SER) approaches put more emphasis on extracting magnitude spectrum-based features, such as Mel Frequency Cepstral Coefficients (MFCCs), and Mel spectrogram. However, phase information is ignored due to signal processing difficulties such as the phase wrapping issue. This work develops a multichannel Convolution Neural Network-Bidirectional Long Short Term Memory (CNN-BLSTM) architectures with an attention mechanism for speaker-independent SER by considering phase and magnitude spectrum-based features. The phase-based features are extracted using the Modified Group Delay Function (MODGD). The obtained phase features are combined with MFCC features. The CNN-BLSTM network extract learned representation from magnitude and phase features. The learned representation from MFCCs and MODGD are combined and given as an input to the Support Vector Machine (SVM) for classification. The Deep Canonical Correlation Analysis (DCCA) is used to maximize the correlation between magnitude and phase information to improve the conventional SER system’s performance. Here the IEMOCAP database is used for performance analysis. The experimental results show improvement over MFCC features and existing approaches for unimodal SER. In this work, we also developed real-time web server application for the proposed architecture.

VIRTUSOFT- The Virtual System

Many modules are developed to assist the physical world interact with the digital world. Here we present a very unique approach for Human Computer Interaction (HCI) where, we control Cursor movement, Keyboard typing with alternative input hardware collectively switch, and a Camera method presented integrates tricks operation like Clicking and Saving the photos in gallery are visiting be performed with the numerous hand gestures by using webcam as data device employing a real-time web camera. During this “VIRTUSOFT” Our method is to use a web camera and computer vision technology, like image segmentation, background subtraction and color tracking to manage mouse tasks (left clicking, right clicking, double clicking and scrolling actions) which we show how it can perform everything as current hardware devices can. For the thing recognition and tracking the color pointer has been used and implement the module with none physical contact with the system. Click events of the mouse are achieved by detecting the quantity of recommendations on the pictures.

An innovative data collection method to eliminate the preprocessing phase in web usage mining

The underlying data source for web usage mining (WUM) is commonly thought to be server logs. However, access log files ensure quite limited data about the clients. Identifying sessions from this messy data takes considerable effort, and operations performed for this purpose do not always yield excellent results. Also, this data cannot be used for web analytics efficiently. This study proposes a method for user tracking, session management, and collecting web usage data. The method is mainly based on an innovative approach for using collected data for web analytics extraction as the data source in web usage mining. An application-based API has been developed with a different strategy from conventional client-side methods to obtain and process log data. The log data has been successfully gathered by integrating the technique into an enterprise web application. The results reveal that the homogeneous data collected and stored with this method is more convenient to browse, filter, and process than web server logs. This structured data can be used effortlessly as a reliable data source for high-performance web usage mining activity, real-time web analytics, machine learning algorithms, or a functional recommendation system.

Presentation Design System to Promote the Lecturer-Audience Interaction in Lessons

Slides are material that supports the information expressed by a speaker. Slides have increased their importance in education due to the multimedia information that we can represent in them and their easy access. Most conference or class presentations do not engage participants in an adequate way. Therefore, this research aims to develop a presentation and survey system in a real-time web application that allows interaction between the teacher-lecturer and the audience in an active way during the session. This research evaluated the system from three approaches: usability, acquired knowledge and user experience. The results suggested that SlideCheck could be an excellent option to encourage students to participate during classes, improve knowledge acquisition and improve interaction with the teacher during the session, making the lesson more dynamic.

Face Mask Detection by Using Convolutional Neutral Net-work and Tensor Flow

Due to this pandemic situation, the WHO introduced that every person must to wear the mask against the deadly virus (COVID-19). The basic aim of our project is to predict the face mask on human face or not. Nowadays, 60% of people are not wearing the mask while going outside. So, it leads to increase positive cases and spreading of disease. Also, rapidly increasing day-by-day because of these critical situations many countries are trying to protect the people but most of them are roaming outside without wearing mask. To overcome this critical situation, we created the Face Mask Detection Model to detect whether the person is wearing mask or not and deployed in Real Time Web application. We implemented the dataset in Convolution Neutral Network and by using Tensor Flow we trained Face Mask Detection Model. Finally, we test the results in Real-Time Web application using Web Framework. Alongside this, we have used concepts of neutral networks and output will be shown in Real Time web application whether the person is wearing mask or not. In our project, we can easily predict the person individually and control the spreading of disease day- by-day. This project can be used in Non-Crowded areas like Malls, Metro, Banks, IT Companies, etc…

(SDGFI) Student’s Demographic and Geographic Feature Identification Using Machine Learning Techniques for Real-Time Automated Web Applications

Nowadays, Google Forms is becoming a cutting-edge tool for gathering research data in the educational domain. Several researchers are using real-time web applications to collect the responses of respondents. Demographic and geographic features are the most important in the researcher’s study. Identifying students’ demographics (gender, age-group, course, institution, or university) and geographic features (locality and country) is a challenging problem in machine learning. We proposed a novel predictive algorithm, Student Demographic Identification (SDI), to identify a student’s demographic features (age-group, course) with the highest accuracy. SDI has been tested on primary reliable samples. SDI has also been compared with the traditional machine algorithms Random Forest (RF), and Logistic Regression (LR), and Radial Support Vector Machine (R–SVM). The proposed algorithm significantly improved the performance metrics such as accuracy, F1-score, precision, recall, and Matthews Correlation Coefficient (MCC) of these classifiers. We also proposed significant features to identify students’ age-group, course, and gender. SDI has identified the student’s age group with an accuracy of 96% and the course with an accuracy of 97%. Gradient Boosting (GB) has improved the accuracy of LR, R-SVM, and RF to predict the student’s gender. Also, the RF algorithm with the support of GB attained the highest accuracy of 98% to identify the gender of the students. All three classifiers have also identified the student’s locality and institution with an identical accuracy of 99%. Our proposed SDI algorithm may be useful for real-time survey applications to predict students’ demographic features.

Comprehensive selective improvements in agri-informatics semantics

The advent of information technology re-innovates all sectors of bio-sciences. Researchers use Semantic Web to improve web searching, mining and integration, which alleviates the time-consuming task of finding relevant and high-quality content. Semantics is improved through ontology engineering in any domain. Amended and developed ontologies will be uploaded to existing standardised and approved biomedical repositories. The establishment of a World Wide Web Consortium (W3C) approved and standardised ontology repository is the most ambitious goal. This work will solely focus on some selected agri-ontologies. The main objective is to promote outcome-based research and transformation styles of relevant expertise sharing. The intended goal is to win project funding to train and equip students with relevant skills and expertise. Need-based and market-oriented training and professional grooming are a tangible asset for students. The majority of traditional Web development freelancers are unaware of ontology or semantic web market demand. Freelancing is another option for expert Ontology developers. However, agriculture students are used to all the research vocabulary and terminologies in their area, but they do not know how to contribute their expertise to improve the efficiency of the Semantic Web in their domain. If the improvement in relevant ontology becomes a part of the Semantic Web, then it is termed ‘Real-time Web semantics enhancement’. In other words, the target ontology becomes a part of the future Web of meaning.