Latent Semantic Analysis Research Articles

A single-cell multiomics analysis of IRF4 in mediating treatment resistance in ibrutinib-treated chronic lymphocytic leukemia (CLL).

7051 Background: The Bruton tyrosine kinase (BTK) inhibitor ibrutinib (ibr) has revolutionized the treatment of CLL, showing efficacy in the majority of patients. Nevertheless, resistance occurs in a subset of patients, often with dismal clinical outcomes. Ibr resistance occurs through several mechanisms, including mutations affecting BTK or PLCG2, or upregulation of alternative survival pathways. We investigated at the single-cell level the molecular mechanisms that underlie ibr resistance in CLL cells and their interactions with non-malignant tumor microenvironment (TME) cell populations. Methods: We retrospectively identified 7 CLL patients with ibr resistance (as second line therapy). Samples were obtained before ibr treatment and at progression. Chromium Single Cell Multiome ATAC + Gene Expression (GEX) (10x Genomics) was applied to a mixture of CD19+CD5+ and CD19- cells to jointly analyze DNA accessibility and gene expression in the same single nuclei of CLL B and TME cells. An average of 4,800 cells (range 2,600-15,000) per sample was retained after QC filtering. Principal component analysis (PCA) was performed on GEX counts. ATAC peaks were called with MACS2, and latent semantic indexing was used for dimensionality reduction. We then computed a weighted nearest neighbour graph, identified clusters using the SLM algorithm and performed cell type assignment with established marker genes. Results: We identified 28 distinct cell clusters, representing CLL B-cells or cell types that constitute the TME, including CD4+ and CD8+ T cells, monocytes, dendritic and NK cells. CLL B-cell clusters were patient and timepoint-specific, while TME clusters were occupied by cells from multiple patients. Five patients showed multiple distinct CLL B-cell clusters pre-treatment or at progression, reflecting cellular heterogeneity within the tumor compartment. Interestingly, some clusters that were dominated by post-treatment CLL B-cells also included cells from pre-treatment samples, suggesting pre-existence of the clone giving rise to the relapse. For each patient, we performed pairwise differential expression between CLL B-cell clusters. Gene ontology analysis revealed increased activation of IRF4 and MHC-related pathways in CLL B-cells at progression compared to pre-treatment in 6 and 4 out of 7 patients, respectively. The elevated IRF4 pathway activity was underpinned by increased chromatin accessibility in IRF4 signature genes. Conclusions: Differential blockade of B-cell receptor signaling by ibr has been reported to downregulate activity of IRF4, a key transcription factor in B-cell activation. Our results suggest that CLL B-cells that are resistant to ibrutinib have re-activated IRF4 activity beyond pre-treatment levels, implying a potential role for IRF4 in ibrutinib resistance.

The Ups and Downs of Black and White: Do Sensorimotor Metaphors Reflect an Evolved Perceptual Interface?

ABSTRACT The Implicit Association Test (IAT) was used to measure population levels of conceptual alignment among two polar sensory metaphors and clusters of concepts to which they are commonly applied. A total of 873 participants were tested online, to compare within- and between-cluster alignments of concepts associated with two different polar sensory metaphors (up/down and black/white). IAT results were sensitive to semantic alignments that were also picked up by Latent Semantic Analysis (LSA) using a large-scale corpus of English. However, even with these semantic alignments taken into account, the dual categorization results demonstrated strong metaphor-cluster alignments over and above the predictions of LSA. It is proposed that, rather than ontogenetic development of metaphoric concepts based on sensorimotor experience, conventionalized sensorimotor metaphors in language may be cognitive tools that provide language learners with insights supported by conceptual properties of a phylogenetically evolved perceptual interface.

Imaginative elaboration in agenesis of the corpus callosum: topic modeling and perplexity.

Previous studies have found deficits in imaginative elaboration and social inference to be associated with agenesis of the corpus callosum (ACC; Renteria-Vasquez et al., 2022; Turk et al., 2009). In the current study, Thematic Apperception Test (TAT) responses from a neurotypical control group and a group of individuals with ACC were used to further study the capacity for imaginative elaboration and story coherence. Topic modeling was employed utilizing Latent Diritchlet Allocation to characterize the narrative responses to the pictures used in the TAT. A measure of the difference between models (perplexity) was used to compare the topics of the responses of individual participants to the common core model derived from the responses of the control group. Story coherence was tested using sentence-to-sentence Latent Semantic Analysis. Group differences in perplexity were statistically significant overall, and for each card individually (p < .001). There were no differences between the groups in story coherence. TAT narratives from persons with ACC were normally coherent, but more conventional (i.e., more similar to the core text) compared to those of neurotypical controls. Individuals with ACC can make conventional social inferences about socially ambiguous stimuli, but are restricted in their imaginative elaborations, resulting in less topical variability (lower perplexity values) compared to neurotypical controls.

Systematic Review: HIV, Aging, and Housing-A North American Perspective, 2012-2023.

Advances in anti-retroviral therapy (ART) have decreased mortality rates and subsequently led to a rise in the number of HIV-positive people living longer. The housing experiences of this new population of interest-older adults (50 years and older) living with HIV-are under-researched. Understanding the housing experiences and unmet needs of older people with HIV can better provide comprehensive care services for them. This study's systematic review evaluated the peer-reviewed literature reporting housing access/insecurity/assistance/options, housing impact, and unmet needs of older individuals living with HIV in North America from 2012 to 2023. Furthermore, Latent Semantic Analysis (LSA), a text-mining technique, and Singular Value Decomposition (SVD) for text clustering were utilized to examine unstructured data from the abstracts selected from the review. The goal was to allow for a better understanding of the relationships between terms in the articles and the identification of emerging public health key themes affecting older adults living with HIV. The results of text clustering yielded two clusters focusing on (1) improvements to housing and healthcare services access and policies and (2) unmet needs-social support, mental health, finance, food, and sexuality insecurities. Topic modeling demonstrated four topics, which we themed to represent (1) a holistic care approach; (2) insecurities-food, financial, sexuality, and other basic needs; (3) access to housing and treatment/care; and (4) homelessness and HIV-related health outcomes. Stable housing, food, and healthcare services access and availability are critical elements to incorporating comprehensive, holistic healthcare for older adults living with HIV. The aging population requires high-priority policies for accessible and equitable healthcare. Clinicians and policymakers should address individual barriers, adopt a patient-centered approach, increase doctor visits, provide competency training, ensure long-term follow-up, involve families, and improve patient education in care management, contributing to HIV/AIDS geriatric care models.

A Comprehensive Review of the Three Main Topic Modeling Algorithms and Challenges in Albanian Employability Skills

Today’s jobseekers face many obstacles while trying to find a career that aligns with their interests, employability soft skills, and professional experience. In Albania, jobseekers frequently initiate their job search by actively exploring job vacancies listed on various online job portals. The analysis of job vacancies posted online provides an added advantage to the labour market actors compared to traditional survey-based analyses. This is because it enables a faster analytical process, promotes decision-making based on accurate data, and should be carefully considered by every country when formulating their Labor Market Policies. Since the data posted online are unlabelled, it has been proven that the potential of unsupervised learning techniques, more precisely the Topic Modelling algorithms, is outstanding when applied to analysing job vacancies, mainly with regard to assessing employability soft skills. Algorithms in topic modelling are essential for uncovering hidden patterns in texts, facilitating the extraction of important data, generating document summaries, and enhancing content comprehension. This paper analyses and compares the three primary methodologies and algorithms used in topic modelling, which can be applied to analyse employability soft-skills: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and BERTopic. At the end of the paper, conclusions are drawn regarding superior performance and optimal algorithm applicability, challenges, and limitations through a review of studies conducted in the Albanian job market.

Grounded Theory Analysis of Online Shopping Reviews Based on Text Clustering

Grounded Theory Analysis of online shopping reviews can improve the quality and accuracy of review data analysis, providing valuable information and suggestions for relevant businesses and consumers. This article focuses on analyzing mobile phone reviews on JD.com as coding objects, effectively employing machine learning algorithms such as Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and text clustering in natural language processing to extract thematic keywords from online shopping review texts and perform axial coding. Subsequently, DBSCAN clustering algorithm is applied to categorize the main categories of design, service, and production under the perspective of businesses and consumers as consumption attraction, while categorizing price and brand under consumption intention, forming selective coding. By applying Grounded Theory to the analysis of online shopping reviews, this study accurately identifies consumer needs and feedback, with the aim of helping businesses improve product and service quality, enhance customer satisfaction and loyalty, and engage in personalized marketing and customized services.

Use of Topic Analysis for Enhancing Healthcare Technologies

Nowadays, technology has played a vital role in the advancement of the healthcare sector. Various healthcare datasets are available on the web in the form of patents, scientific papers, articles, textual feedback, chatlogs, abstracts of papers, medical reports, and social media posts. It is a tedious task for the stakeholders to find hidden crucial knowledge on the discussed topic from this content, which if utilized optimally can lead to the rapid development of the healthcare sector. Topic analysis concepts are very effective in extracting meaningful topics from the data. Here, frequently applied Topic modeling methods -Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation, Correlated Topics Model, and Non-negative matrix factorization are surveyed along with their benefits and drawbacks. Insights on new innovative topic modeling techniques used in healthcare with their objective, opportunities, and challenges are provided, which can help the researchers for the enhancement of healthcare facilities.

Automatic Extractive Text Summarization using Multiple Linguistic Features

Automatic text summarization (ATS) provides a summary of distinct categories of information using natural language processing (NLP). Low-resource languages like Hindi have restricted applications of these techniques. This study proposes a method for automatically generating summaries of Hindi documents using extractive technique. The approach retrieves pertinent sentences from the source documents by employing multiple linguistic features and machine learning (ML) using maximum likelihood estimation (MLE) and maximum entropy (ME). We conducted pre-processing on the input documents, such as eliminating Hindi stop words and stemming. We have obtained 15 linguistic feature scores from each document to identify the phrases with high scores for summary generation. We have performed experiments over BBC News articles, CNN News, DUC 2004, Hindi Text Short Summarization Corpus, Indian Language News Text Summarization Corpus, and Wikipedia Articles for the proposed text summarizer. The Hindi Text Short Summarization Corpus and Indian Language News Text Summarization Corpus datasets are in Hindi, whereas BBC News articles, CNN News, and the DUC 2004 datasets have been translated into Hindi using Google, Microsoft Bing, and Systran translators for experiments. The summarization results have been calculated and shown for Hindi as well as for English to compare the performance of a low and rich-resource language. Multiple ROUGE metrics, along with precision, recall, and F-measure, have been used for the evaluation, which shows the better performance of the proposed method with multiple ROUGE scores. We compare the proposed method with the supervised and unsupervised machine learning methodologies, including support vector machine (SVM), Naive Bayes (NB), decision tree (DT), latent semantic analysis (LSA), latent Dirichlet allocation (LDA), and K-means clustering, and it was found that the proposed method outperforms these methods.

Generating images using generative adversarial networks based on text descriptions

Modern developments in the fields of natural language processing (NLP) and computer vision (CV) emphasize the increasing importance of generating images from text descriptions. The presented article analyzes and compares two key methods in this area: generative adversarial network with conditional latent semantic analysis (GAN-CLS) and ultra-long transformer network (XLNet). The main components of GAN-CLS, including the generator, discriminator, and text encoder, are discussed in the context of their functional tasks—generating images from text inputs, assessing the realism of generated images, and converting text descriptions into latent spaces, respectively. A detailed comparative analysis of the performance of GAN-CLS and XLNet, the latter of which is widely used in the organic light-emitting diode (OEL) field, is carried out. The purpose of the study is to determine the effectiveness of each method in different scenarios and then provide valuable recommendations for selecting the best method for generating images from text descriptions, taking into account specific tasks and resources. Ultimately, our paper aims to be a valuable research resource by providing scientific guidance for NLP and CV experts.

Analysis of Suicide-related Tweets During the COVID-19 Pandemic

Background:: The COVID-19 virus started in 2019 and badly affected the different sectors of many countries around the world. Based on this, financial difficulties, loss of loved ones, sudden anger, relationships, family disputes, and psychological distress increased, and individuals were stalled from carrying out their lifestyle in a normal way, and some individuals were even motivated to commit suicide. Objective:: It is important to reduce the number of suicides and identify the reasons for this situation. Through this research, the focus is on identifying the main topics discussed relevant to suicides during the COVID-19 pandemic. Methods:: Individuals use Twitter, a social media platform, to share their ideas freely and publically. We collected 9750 primary data through Twitter API (Application Programming Interface). After preprocessing and feature extraction by TF-IDF (Term Frequency-Inverse Document Frequency), we applied the LDA (Latent Dirichlet Allocation) and Probabilistic Latent Semantic Analysis (PLSA) topic modeling algorithms to identify topics. Results:: Based on the LDA results, we extracted ten different topics under the three themes, such as the impact of COVID-19, human feelings, getting support, and having awareness. Intertopic Distance Map, Most Salient Terms, and Word Clouds Visualization are used to check the results. The coherence score and perplexing value are used to measure how interpretable the extracted topics are to humans. PLSA also extracted 25 topics with their probabilities, and Kullback–Leibler (KL) divergence was used to check the results. Conclusion:: We were able to gain insight into human emotions and the main motivations behind suicide attempts using the topics we extracted. Expert feedback proved that LDA results were better than PLSA. Based on that, we found the main impact of COVID-19 on human lives, how human feelings were changed positively and negatively during that period, what supporting and awareness methods people used, and what they preferred. The required measures can then be taken by those responsible authorities and individuals to prevent, reduce, and get ready for this kind of suicidal incident in the future.

A Comprehensive Analysis and Visualization of Trends and Research Patterns in the Field of IoT Smart Cities

Every nation is interested in creating "smart cities," making this a hot topic requiring extensive scientific study. Given the current state of affairs, it is essential to conduct a systematic review to thoroughly understand the current research trends and patterns in this field. It is applied to a corpus of 9,131 papers published between 2010 and 2022 from the Scopus publications database to test the string's effectiveness. This research utilizes text mining and Latent Semantic Analysis (LSA) to delve into the current state-of-the-art study concerning IoT Smart cities research. KNIME was used to conduct the analysis. Predicting the study domain is done with the help of the K-means clustering method. Future researchers can build upon these patterns to strengthen security in various sectors. This report also reviews the history of smart city research, its current position, and its prospects. We help institutions and authors collaborate globally in science. This research analyses Smart City IoT integration trends and patterns in a graphical overview. According to the data, the identified areas are developing and need more research.

Advancing Message Board Topic Modeling Through Stack Ensemble Techniques

In the digital era, message boards serve as vital hubs for diverse discussions, knowledge dissemination, and community interaction. However, navigating the vast and varied content on these platforms presents a formidable challenge. This research pioneers the utilization of stack ensemble techniques to revolutionize topic modeling on message board data. Integrating Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), and Latent Semantic Analysis (LSA) within a sophisticated ensemble framework, this study introduces a paradigm shift in extracting nuanced insights. Incorporating domain-specific features, sentiment analysis, and temporal patterns enriches contextual understanding. Rigorous evaluation across diverse message board datasets underscores the ensemble method's unparalleled accuracy, stability, and interpretability, setting a new standard for discourse analysis in online communities. Keywords: Topic Modeling, Latent Dirichlet Allocation, Stack Ensemble Techniques, Natural Language Processing, Message Boards, Ensemble Learning

An experimental study for identifying customer prominent viewpoints on different flight classes by topic modeling methods

The airline industry has a wide range of customers with different tastes and expectations of airline services. Therefore, knowing the different attitudes of customers and consequently addressing their opinions is paramount in this business. Airlines offer different flight classes to customers to provide passenger transportation services and use different methods to evaluate customer satisfaction. One of the methods used to analyze customer opinions is the use of text analysis with machine learning techniques. In this study, to analyze the textual data, we used topic modeling to identify passengers' attitudes. In the first stage, we evaluated the performance of three different topic modeling methods: Latent Semantic Indexing (LSI), Hierarchical Dirichlet Process (HDP), and Latent Dirichlet Allocation (LDA). As a result of the modeling process, LDA had the best performance with an average coherence value of 0.42. Eventually, after determining the optimum number of topics as 10 topics, the prominent topics were interpreted and explained. The results of this study demonstrate the common and distinct attitudes of customers of economy class, business class, and first class. As appeared in the results of the economy flight class, the most important topic that appeared in comments was "passenger care". However, in whole reviews and reviews of business and first flight classes, the topic of "time" was more repetitive. Given the wide application of the presented methodology, the findings of this research can provide airline managers with a broader insight into passenger preferences and guide them on how to provide quality service for their customers.

Discovering significant topics from legal decisions with selective inference.

We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distributions which can be manually interpreted to gain insights about significant topics, and case-topic weights which can be used to identify representative cases for each topic. We demonstrate the method on a new dataset of domain name disputes and a canonical dataset of European Court of Human Rights violation cases. Topic models based on latent semantic analysis as well as language model embeddings are evaluated. We show that topics derived by the pipeline are consistent with legal doctrines in both areas and can be useful in other related legal analysis tasks. This article is part of the theme issue 'A complexity science approach to law and governance'.

Impact of subjective well-being on physical frailty in middle-aged and elderly Japanese with high social isolation.

Social isolation exacerbates physical frailty and is associated with subjective well-being. Even those with high levels of social isolation may have different health statuses depending on the type of isolation and their subjective well-being. However, the effect of subjective well-being on the relationship between social isolation and physical frailty remains unclear. This study examined whether the risk of physical frailty was the same for individuals with social isolation according to high and low subjective well-being. The study participants included 1,953 middle-aged Japanese adults aged 45 years and older. Physical frailty was assessed using a modified version of the Fried phenotype criteria. Probabilistic Latent Semantic Analysis was used to classify participants according to social isolation indicators. Subsequently, we focused on the groups with high social isolation and classified them according to whether their subjective well-being was high or low. Subjective well-being was evaluated using the Shiawase and Ikigai scales, which are concepts used in Japan. Finally, we used survival time analysis to examine the relationship between Shiawase or Ikigai and physical frailty in groups with high social isolation. The participants were classified into four groups based on their social isolation status. The physical frailty rate of the high social isolation class was 37.0%, which was significantly higher than that of the other classes. Survival time analysis revealed that among people with high social isolation, those with high Shiawase and Ikigai had a significantly lower risk of physical frailty than those with low Shiawase and Ikigai. All individuals with high social isolation are not at a high risk of physical frailty. The findings reveal that even those with high level of social isolation may have a lower risk of physical frailty if their subjective well-being is high. These results will contribute to promoting the prevention of frailty in middle-aged and older adults.

Online Information Reviews to Boost Tourism in the B&amp;B Industry to Reveal the Truth and Nexus

Grasping the concerns of customers is paramount, serving as a foundation for both attracting and retaining a loyal customer base. While customer satisfaction has been extensively explored across diverse industries, there remains a dearth of insights into how distinct rural bed and breakfasts (RB&amp;Bs) can effectively cater to the specific needs of their target audience. This research utilized latent semantic analysis and text regression techniques on online reviews, uncovering previously unrecognized factors contributing to RB&amp;B customer satisfaction. Furthermore, the study demonstrates that certain factors wield distinct impacts on guest satisfaction within varying RB&amp;B market segments. The implications of these findings extend to empowering RB&amp;B owners with actionable insights to enhance the overall customer experience.

Supervised probabilistic latent semantic analysis with applications to controversy analysis of legislative bills

Probabilistic Latent Semantic Analysis (PLSA) is a fundamental text analysis technique that models each word in a document as a sample from a mixture of topics. PLSA is the precursor of probabilistic topic models including Latent Dirichlet Allocation (LDA). PLSA, LDA and their numerous extensions have been successfully applied to many text mining and retrieval tasks. One important extension of LDA is supervised LDA (sLDA), which distinguishes itself from most topic models in that it is supervised. However, to the best of our knowledge, no prior work extends PLSA in a similar manner sLDA extends LDA by jointly modeling the contents and the responses of documents. In this paper, we propose supervised PLSA (sPLSA) which can efficiently infer latent topics and their factorized response values from the contents and the responses of documents. The major challenge lies in estimating a document’s topic distribution which is a constrained probability that is dictated by both the content and the response of the document. To tackle this challenge, we introduce an auxiliary variable to transform the constrained optimization problem to an unconstrained optimization problem. This allows us to derive an efficient Expectation and Maximization (EM) algorithm for parameter estimation. Compared to sLDA, sPLSA converges much faster and requires less hyperparameter tuning, while performing similarly on topic modeling and better in response factorization. This makes sPLSA an appealing choice for latent response analysis such as ranking latent topics by their factorized response values. We apply the proposed sPLSA model to analyze the controversy of bills from the United States Congress. We demonstrate the effectiveness of our model by identifying contentious legislative issues.

Exploring the importance of mobile app attributes based on consumers' voices using structured and unstructured data

PurposeThis exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches explore users' behavior and attitudes toward the priorities of mobile app attributes and preferences, identifying correlations between attributes and aggregating individual attributes into groups.Design/methodology/approachOnline convenience sampling and snowball sampling resulted in 417 valid responses. The numerical data are analyzed using the relative to an identified distribution (RIDIT) scoring system and gray relational analysis (GRA), and qualitative responses are investigated using text-mining techniques.FindingsThis study finds enhanced nuances of user preferences and provides data-driven insights that might help app developers and marketers create a distinct app that will add value to consumers. The latent semantic analysis indicates relationship structure among the attributes, and text-based cluster analysis determines the subsets of attributes that represent the unique functions of the mobile app.Practical implicationsThis study reveals the essential components of mobile apps, paying particular attention to the consumer value component, which boosts user approval and encourages prolonged use. Overall, the results demonstrate that developers must concentrate on its functional, technical and esthetic features to make an app more exciting and practical for potential users.Originality/valueMost scholarly research on apps has focused on their technological merits, aesthetics and usability from the user's perspective. A post-adoption multi-attribute app analysis using both structured and unstructured data is conducted in this study.

Sistem Pendukung Pendeteksi Kebersihan Lingkungan Menggunakan Metode LSI

Technology is a tool that really helps human needs to carry out daily activities very easily, quickly, and practically. Monitoring data on the cleanliness of the Kerinci Pangkalan Environment is still being carried out one by one, namely in a conventional way that has not been systemized or computerized. The large amount of cleanliness data makes the Pelalawan Environmental Service often have difficulty detecting the cleanliness of the Pangkalan Kerinci Environment. This causes much needed computer technology using a detection system. This detection is one way to detect cleanliness. The purpose of this study is to facilitate field officers in detecting the environmental cleanliness of Pangkalan Kerinci. The data collection is in the form of literature studies, interviews and observations. Quantitative methods are used in this research process to be used in conducting testing. The Pangkalan Kerinci Environmental Cleanliness Detection Support System was built with the help of Latent Semantic Indexing (LSI) and firebase as a data repository. The Pangkalan Kerinci Environmental Cleanliness Detection Support System was tested using blackbox testing which was successful and a User Accaptance Test (UAT) was carried out with an average score of 90.8% from 20 respondents with the title "Very Good" which means that the officers from the Pangkalan Kerinci Environmental Cleaning Service are satisfied with the Kerinci Base Environment&#x0D;

Horizoning recent trends in the security of smart cities: Exploratory analysis using latent semantic analysis

The expeditious advancement and widespread implementation of intelligent urban infrastructure have yielded manifold advantages, albeit concurrently engendering novel security predicaments. Examining current patterns in the security of smart cities is paramount in comprehending nascent risks and formulating efficacious preventative measures. The present study suggests the utilization of Latent Semantic Analysis (LSA) as a means to scrutinize and reveal implicit semantic associations within a collection of textual materials pertaining to the security of smart cities. Through the process of gathering and pre-processing pertinent textual data, constructing a matrix that represents the frequency of terms within documents, and utilizing techniques that reduce the number of dimensions, Latent Semantic Analysis (LSA) has the ability to uncover concealed patterns and associations among concepts related to security. This study proposes five recommendations for future research that employ a topic modeling technique to investigate the often-explored subjects related to smart city security. This discovery provides additional evidence in favor of the proposition that a robust blockchain-driven framework is vital for the advancement of smart cities. Latent Semantic Analysis (LSA) offers important insights into the dynamic landscape of smart city security by employing several techniques such as pattern recognition, document or phrase clustering, and result visualization. Through the examination of patterns and developments, individuals in positions of political authority, urban planning, and security knowledge possess the ability to uphold their proficiency, render judicious choices substantiated by empirical data, and establish proactive strategies aimed at preserving the security, privacy, and sustainability of intelligent urban environments.