مطالب

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.

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.

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.