International Journal of Web Research
Volume:6 Issue: 2, Autumn-Winter 2023

Blockchain is a technology that enables distributed and secure data structures for various business domains. Bitcoin is a notable blockchain application that is a decentralized digital currency with immense popularity and value. Bitcoin involves many concepts and processes that require modelling for better comprehension and development. Modelling is a technique that simplifies and abstracts a system at a certain level of detail and accuracy. Software modelling is applied in Model-Driven Engineering (MDE), which automates the software development process using models and transformations. Domain-specific languages (DSLs) are languages that are customized for a specific domain and offer intuitive syntax for domain experts.  To address the need for specialized tools for Bitcoin blockchain modelling, we propose a novel Unified Modelling Language (UML) profile that is specifically designed for this domain. UML is a standard general-purpose modelling language that can be extended by profiles to support specific domains. A meta-model is a model that defines the syntax and semantics of a modelling language. The proposed meta-model, which includes stereotypes, tagged values, enumerations, and constraints defined by Object Constraint Language (OCL), is defined as a UML profile. The proposed meta-model is implemented in the Sparx Enterprise Architect (Sparx EA) modelling tool, which is a widely used tool for software modelling and design. To validate the practicality and effectiveness of the proposed UML profile, we developed a real-world case study using the proposed meta-model and conducted an evaluation using the Architecture Tradeoff Analysis Method (ATAM). The results showed the proposed UML profile promising.

Given a limited understanding of post-COVID customer behavior on social media, this paper seeks to identify consumer behavioral patterns on Facebook and Instagram in the post-COVID era. The research explores all the inner and outer features that might affect the way consumers think or act in the post-COVID era. Hence, a qualitative approach has been adopted to have a more comprehensive and current understanding. The research begins by identifying pertinent factors shaping consumer behavior through an extensive review of existing literature. To refine and validate these factors, the study employs the fuzzy Delphi method, a research technique that involves seeking consensus among a panel of experts. The findings of the research illuminate 24 distinct factors, organized into five main dimensions: contextual, social, content, technological, and product. Notably, the study underscores the significance of product-related factors, technological advancements, content quality, and social interactions in shaping post-COVID consumer behaviors. The product-related factors hold the most substantial influence over consumer behaviors, followed by technological considerations, content quality, and social interactions. Managers should prioritize quality assurance and strategic product categorization. Emphasizing these aspects in product positioning can significantly influence consumer behavior on social media platforms.

The capital market plays a crucial role within a country's financial structure and is instrumental in funding significant, long-term projects. Investments in the railway transport industry are vital for boosting other economic areas and have a profound impact on macroeconomic dynamics. Nonetheless, the potential for delayed or uncertain returns may deter investors. Accurate predictions of rail company stock prices on exchanges are therefore vital for making informed investment choices and securing sustained investment. This study employs deep learning techniques to forecast the closing prices of MAPNA and Toucaril shares on the Tehran Stock Exchange. It utilizes deep neural networks, specifically One-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM) networks, and a combined CNN-LSTM model, for stock price prediction. The effectiveness of these models is measured using various metrics, including MAE, MSE, RMSE, MAPE, and R2. Findings indicate that deep learning methods can predict stock prices effectively, with the CNN-LSTM model outperforming others in this research. According to the results, The CNN-LSTM model reached the highest R2 of 0.992. Also, based on criteria such as MAE, MSE, RMSE, and MAPE the best results belong to LSTM (Kaggle-modified) with 521.715, 651119.194, 806.920, and 0.028, respectively.

In recent years, determining the most effective time to post on Instagram for increased engagement has become a central concern for digital marketers. Despite its significance, research on this topic remains limited. This study adopts an exploratory research approach to analyze Instagram posts from selected Western countries (Europe and America) and Iranian businesses. The data were collected during the period from February 21, 2022, to March 21, 2022, employing web scraping tools. Classification algorithms, including XGBoost, K-NN, SVM, and Linear Regression, are employed for modeling, with results favoring the XGBoost method for accuracy. The study reveals optimal posting times between 12 and 3 pm for Western Countries businesses and 9 and 12 am for Iranian businesses. Furthermore, it suggests Sunday as the best day for posting in the West, contrasting with Thursday in Iran. In summary, this research underscores the differing ideal posting times in Iran and the West, emphasizing the challenge of constructing a uniform model for all countries.

In order to resolve the issues with Intrusion Detection Systems (IDS), a preprocessing step known as feature selection is utilized. The main objectives of this step are to enhance the accuracy of classification, improve the clustering operation on imbalance dataset and reduce the storage space required. During feature selection, a subset of pertinent and non-duplicative features is chosen from the original set. In this paper, a novel approach for feature selection in intrusion detection is introduced, leveraging an enhanced k-means clustering algorithm. The clustering operation is further improved using the combination of Gravity Search Algorithm (GSA) and Particle Swarm Optimization (PSO) techniques. Additionally, Biogeography Based Optimization (BBO) technique known for its successful performance in addressing classification problems is also employed. To evaluate the proposed approach, it is tested on the UNSW-NB15 intrusion detection dataset. Finally, a comparative analysis is conducted, and the results demonstrate the effectiveness of the proposed approach, in such a way that the value of the detection accuracy parameter in the proposed method was 99.8% and in other methods it was a maximum of 99.2%.

Contemporary machine learning models, like deep neural networks, require substantial labeled datasets for proper training. However, in areas such as natural language processing, a shortage of labeled data can lead to overfitting. To address this challenge, data augmentation, which involves transforming data points to maintain class labels and provide additional valuable information, has become an effective strategy. In this paper, a deep reinforcement learning-based text augmentation method for sentiment analysis was introduced, combining reinforcement learning with deep learning. The technique uses Deep Q-Network (DQN) as the reinforcement learning method to search for an efficient augmentation strategy, employing four text augmentation transformations: random deletion, synonym replacement, random swapping, and random insertion. Additionally, various deep learning networks, including CNN, Bi-LSTM, Transformer, BERT, and XLNet, were evaluated for the training phase. Experimental findings show that the proposed technique can achieve an accuracy of 65.1% with only 20% of the dataset and 69.3% with 40% of the dataset. Furthermore, with just 10% of the dataset, the method yields an F1-score of 62.1%, rising to 69.1% with 40% of the dataset, outperforming previous approaches. Evaluation on the SemEval dataset demonstrates that reinforcement learning can efficiently augment text datasets for improved sentiment analysis results.

Data streams are continuous flows of data objects generated at high rates, requiring real-time processing in a single pass. Clustering algorithms play a vital role in analyzing data streams by grouping similar data samples. Among various time windows for evolving streams, the sliding window method gradually moves over the data, focusing on the most recent information and improving clustering accuracy while reducing memory requirements. The development of distributed computing frameworks like Apache Spark has addressed the limitations of traditional tools in processing big data, including data streams. This paper presents the DynamicCluStream algorithm, an enhancement over Spark-CluStream, which employs a two-phase clustering approach with precise clustering of recent data. The algorithm dynamically determines the number of clusters by merging overlapping clusters during the offline phase, resulting in significant improvements in cluster precision. Experimental results show that it performs up to 47 percent better on average in terms of precision on the CoverType dataset and up to 92 percent better on average in terms of precision on the PowerSupply dataset.  Although the algorithm is slower due to data sample removal and cluster integration, its impact is negligible in a distributed environment.

The anticipated integration of 6G technology within the telecommunications sector is poised to significantly enhance communication capabilities in the forthcoming years. The proliferation of 6G within Etisalat's infrastructure is expected to concurrently drive the expansion of the Internet of Things (IoT), facilitating its operation across a diverse array of mobile and stationary devices. Within the IoT domain, particularly under the 6G framework, certain applications necessitate real-time operation and thus warrant prioritization over others in terms of communication and data transmission. The strategic clustering of users, based on assigned weight factors, can bolster the prioritization process, thereby optimizing the efficiency of real-time applications. This paper delineates methodologies for expediting user connectivity—termed 'real-time'—and delineates them from non-time-critical applications. The implementation of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed as a viable strategy for clustering IoT devices, thereby managing the increased volume of smaller, more granular data packets characteristic of 6G networks. Utilizing DBSCAN clustering facilitates the preemptive identification of potential user congestion and traffic, enabling the deployment of the outlined strategies to mitigate service degradation and maintain data transfer rates. This research explores the formulation of a prioritized scheduling system for requests, wherein, as per the DBSCAN algorithm, real-time applications are accorded elevated execution precedence.

Computational aesthetics is a field that combines science and art to explore aesthetic measurement, generative art, and design generation using computational methods. In the context of university library websites, adhering to aesthetic standards, particularly focusing on "moderate visual complexity," could enhance their visibility online (according to some previous studies). This research, analyzed 82 university library websites, including top international and Iranian academic libraries, to assess visual complexity based on Berlyne's theory of stimulus complexity using the Athec Python library. The study found that international university libraries have a complexity of over 0.57, while Iranian academic libraries lack the minimum complexity needed to motivate users. Moreover, the study found significant differences between the library websites of top Iranian and international universities. The linear regression statistic test was used to analyze the relationship between the visual complexity of academic library websites and the rank of the university, revealing a significant difference for the 41 top Iranian universities but not for the 41 top international universities. The Beta coefficient of linear regression between the visual complexity of academic library websites and the rank of the university is -0.502, and Sig=0.001, obtained for the top Iranian universities. On the other hand, the Beta coefficient of linear regression between the visual complexity of academic library websites and the ranks of the university is -0.062, and Sig=0.701, obtained for top international universities. This research highlights the innovation of connecting Berlyne’s theory of stimulus complexity with Python programming, providing a new perspective for university library website managers.

Heart disease has emerged as the foremost global cause of mortality. Enhancing awareness and understanding of this disease, along with preventive strategies, is pivotal in averting its onset and diminishing mortality rates. Today, social networks have evolved into paramount information dissemination platforms owing to their user-friendly nature and widespread adoption. Among these, Twitter stands out as a prominent source of rich data that can be leveraged for educational and awareness purposes. While existing studies have evaluated the impact of social media on increasing health-related knowledge, there is a gap in research regarding the role of Twitter in increasing cardiovascular disease awareness and prevention from different perspectives. This study employs a cross-sectional and descriptive methodology to quantitatively analyze over 50026 tweets.  This study seeks to investigate how Twitter users seek and disseminate information related to cardiovascular diseases. It aims to identify the prevalent topics shared about cardiovascular diseases and analyze the content of these messages.  Initially, 50026 tweets from 8,619 users were gathered over a one-month timeframe. English tweets have been selected due to the prevalence of the English language. Subsequently, the tweets were categorized and analyzed utilizing the LDA technique and the MALLET platform. Content analysis was conducted across various categories, focusing on topics, temporal trends, and geographical locations of the tweets. The results show that there was a significant relationship between the parameters extracted in the research and the most concern of users was in the field of heart diseases and prevention methods. Most user tweets (36,323 or 72.60%) contained specific information about heart disease. 9.33% related to cardiovascular issues, 2817 (5.63%) tweets were about heart attack, 2949 (5.89%) were about heart failure and 3267 (6.5%) about other cases related to heart disorders (cardiac arrest, cardiomyopathy, ischemic heart, etc.). The most concern of users in the group of heart diseases was related to the connection of topics such as cholesterol (4102 tweets (11.04%)), prevention (20348 tweets (56.01%)) and diet (1114 (3.06%)) with heart disease.

The proliferation of e-commerce has led to an overwhelming volume of customer reviews, posing challenges for consumers who seek reliable product evaluations and for businesses concerned with the integrity of their online reputation. This study addresses the critical problem of detecting fake reviews by developing a comprehensive framework that integrates Natural Language Processing (NLP) and machine learning techniques. Our methodology centers on sentiment analysis to discern the emotional valence of reviews, coupled with Part-of-Speech (PoS) tagging to analyze linguistic patterns that may signal deception. We meticulously extract a rich set of textual and statistical features, providing a robust basis for our predictive models. To enhance classification performance, we strategically employ both traditional machine learning algorithms and powerful ensemble techniques. Experimental results underscore the efficacy of our approach in detecting fraudulent reviews. We achieved a notable F1-Score of 82.9% and an accuracy of 82.6%, demonstrating the potential to safeguard consumers from misleading information and protect businesses from unfair practices.

Lung infection represents one of the most perilous indicators of Covid-19. The most efficient diagnostic approach entails the analysis of CT scan images. Utilizing deep learning algorithms and machine vision, computer scientists have devised a method for automated detection of this disease. This study proposes a two-stage approach to identifying lung infection. In the initial stage, image features are extracted through a transfer learning framework employing ResNet50, with the last two layers being fixed. Subsequently, a CNN neural network is constructed for image detection and categorization in the second stage. By employing superior image feature selection and minimizing non-informative features, this proposed method achieves impressive accuracy metrics: 98.99% accuracy, 98.91% sensitivity, and 99.10% specificity. Furthermore, a comparative analysis is conducted between this method and six other architectures (Inception, InceptionResNetV2, ResNet101, ResNet152, VGG16, VGG19), with and without transfer learning. The findings demonstrate that the proposed method attains 98% accuracy on test data, without succumbing to overfitting.

The surge in internet usage has sparked new demands. Historically, specialized web crawlers were devised to retrieve pages pertaining to specific subjects. However, contemporary needs such as event identification and extraction have gained significance. Conventional web crawlers prove inadequate for these tasks, necessitating exploration of novel techniques for event identification, extraction, and utilization. This study presents an innovative approach for detecting and extracting events using the Whale Optimization Algorithm (WOA) for feature extraction and classification. By integrating this method with machine learning algorithms, the proposed technique exhibits improvements in experiments, including decreased execution time and enhancements in metrics such as Root Mean Square Error (RMSE) and accuracy score. Comparative analysis reveals that the proposed method outperformed alternative models. Nevertheless, when tested across various data models and datasets, the WOA model consistently demonstrated superior performance, albeit exhibiting reduced evaluation metrics for Wikipedia text data.