Behavior Analysis Techniques Research Articles

Online Reviews Meet Visual Attention: A Study on Consumer Patterns in Advertising, Analyzing Customer Satisfaction, Visual Engagement, and Purchase Intention

This study aims to bridge the gap between traditional consumer behavior analysis and modern techniques by integrating big data analysis, eye-tracking technology, and survey methods. The researchers considered that understanding consumer behavior is crucial for creating effective advertisements in the digital age. Initially, a big data analysis was performed to identify significant clusters of consumer sentiment from online reviews generated during a recent seasonal promotional campaign. The key factors were identified and grouped into the “Product”, “Model”, “Promo”, and “Effect” categories. Using these clusters as a foundation, an eye-tracking analysis measured visual attention metrics such as the fixation duration and count to understand how the participants engaged with the different advertisement content. Subsequently, a survey assessed the same participants’ purchase intentions and preferences related to the identified clusters. The results showed that the sentiment clusters related to products, promotions, and effects positively impacted the customer satisfaction. The eye-tracking data revealed that advertisements featuring products and models garnered the most visual attention, while the survey results indicated that promotional content significantly influenced the purchase intentions. This multi-step approach delivers an in-depth understanding of the factors that affect customer satisfaction and decision-making, providing valuable information for optimizing marketing strategies in the Korean skincare market. The findings emphasize the importance of integrating consumer sentiment analysis with visual engagement metrics to develop more effective and compelling marketing campaigns.

ANALYZING USER BEHAVIOR PATTERNS FOR PERSONALIZED RECOMMENDER SYSTEMS IN E-COMMERCE: A LITERATURE REVIEW

Abstract: E-commerce thrives on a user-centric strategy, and recommender systems are at the cutting edge of personalizing the purchasing experience. These systems may forecast preferences and recommend appropriate items by analyzing user behavior patterns, resulting in many benefits such as increased customer satisfaction, increased sales and conversions, and increased efficiency. To accomplish these benefits, recommender systems utilize complex algorithms that examine numerous elements of user behavior such as purchase history, browsing behavior, search queries, demographic data, and implicit feedback. Sophisticated algorithms can recognise complicated patterns in user data, resulting in more accurate and personalized suggestions. Analyzing user reviews, product descriptions, and social media interactions may help you better understand consumer preferences and product features. Systems can make real-time suggestions depending on a user’s current browsing session, resulting in a more dynamic purchasing experience. Personalized recommender systems will play an increasingly important role in molding the future of e-commerce as user behavior analysis techniques are constantly refined. The study intends to make important advances to the field of personalized recommender systems by undertaking a thorough research of user behavior patterns in the e-commerce domain. We strive to improve the performance of recommender systems by extracting insightful features from various data sources and exploring sophisticated machine learning techniques, resulting in a more engaging and tailored user experience that fosters customer satisfaction and drives business growth. A comprehensive review of user behavior patterns and their influence on personalized recommender systems in the e-commerce industry reveals the critical role of data analysis and machine learning algorithms in tailoring product suggestions to individual preferences, thereby enhancing customer satisfaction and driving sales growth. By implementing the tactics and approaches expressed in this study, e-commerce platforms may stay ahead of the curve, providing a smooth and tailored purchasing experience that surpasses customer expectations and contributes to their competitive advantage in the changing e-commerce environment.

Oculometric biomarkers of visuomotor deficits in clinically asymptomatic patients with systemic lupus erythematosus undergoing long-term hydroxychloroquine treatment.

This study examines a set of oculomotor measurements, or "oculometric" biomarkers, as potential early indicators of visual and visuomotor deficits due to retinal toxicity in asymptomatic Systemic Lupus Erythematosus (SLE) patients on long-term hydroxychloroquine (HCQ) treatment. The aim is to identify subclinical functional impairments that are otherwise undetectable by standard clinical tests and to link them to structural retinal changes. We measured oculomotor responses in a cohort of SLE patients on chronic HCQ therapy using a previously established behavioral task and analysis technique. We also examined the relationship between oculometrics, OCT measures of retinal thickness, and standard clinical perimetry measures of visual function in our patient group using Bivariate Pearson Correlation and a Linear Mixed-Effects Model (LMM). Significant visual and visuomotor deficits were found in 12 asymptomatic SLE patients on long-term HCQ therapy compared to a cohort of 17 age-matched healthy controls. Notably, six oculometrics were significantly different. The median initial pursuit acceleration was 22%, steady-state pursuit gain 16%, proportion smooth 7%, and target speed responsiveness 31% lower, while catch-up saccade amplitude was 46% and fixation error 46% larger. Excluding the two patients with diagnosed mild toxicity, four oculometrics, all but fixation error and proportion smooth, remained significantly impaired compared to controls. Across our population of 12 patients (24 retinae), we found that pursuit latency, initial acceleration, steady-state gain, and fixation error were linearly related to retinal thickness even when age was accounted for, while standard measures of clinical function (Mean Deviation and Pattern Standard Deviation) were not. Our data show that specific oculometrics are sensitive early biomarkers of functional deficits in SLE patients on HCQ that could be harnessed to assist in the early detection of HCQ-induced retinal toxicity and other visual pathologies, potentially providing early diagnostic value beyond standard visual field and OCT evaluations.

Unveiling The Detection of File Less Malware from Dark Web: A Stealthy Arsenal for Web Application Exploitation

The proliferation of fileless malware poses a significant threat to web application security, especially when sourced from the dark web. This paper presents a novel detection tool designed to identify and mitigate fileless malware sourced from the dark web, specifically targeting web applications. Leveraging advanced anomaly detection and behavioral analysis techniques, the tool monitors real-time traffic and user interactions, enabling the early detection of malicious activities. Additionally, the tool integrates seamlessly with threat intelligence sources, enhancing its ability to recognize emerging threats. A user-centric alerting system ensures that administrators are promptly notified of potential security breaches, allowing for immediate remediation actions. The tool's adaptability and continuous enhancement mechanisms ensure that it stays ahead of evolving threats. By employing this detection tool, organizations can bolster their web application security posture and protect sensitive data from sophisticated fileless malware attacks.

Psychological behavioral therapies to improve autistic children’s behaviors during dental visits: A systematic review and meta-analysis

This systematic review aimed to evaluate the effectiveness of psychological behavioral therapies to improve behaviors of autistic children during dental visits. Screening, data extraction, risk-of-bias assessment, and evaluation of certainty of evidence were performed by two independent reviewers. Eighteen studies were ultimately included. On average 56% (95% confidence interval, 33.95–78.29) and 64% (95% confidence interval 47.24–81.80) of autistic children can cooperate with dental examination with an oral mirror in the first and second visits, respectively. Visual pedagogy did not improve the proportion of autistic children who can accept dental examinations. Inconsistent results were also identified for Treatment and Education of Autistic and related Communication-handicapped CHildren, Picture Exchange Communication System, Applied Behavior Analysis, video modeling, distractions, and other conventional behavioral management techniques. Many studies were of small sample size, lacked a control group, did not account for autism spectrum disorder severity, comorbidities, previous dental experience or other confounders, and had a high risk of bias. The evidence supporting the use of psychoeducation techniques to improve the dental experience of autistic children is limited and of very low certainty. Further research with larger scale studies and appropriate control groups is needed to enhance the certainty of evidence in this field. Lay abstract This research review looked at how well different psychological behavioral therapies help improve the behavior of autistic children during dental visits. The researchers studied 18 different studies and found that, on average, about 56% of autistic children were able to cooperate with a dental exam using an oral mirror during their first visit. The number increased to about 64% during their second visit. However, using visual pedagogies or teaching aids did not seem to make a big difference in how many children could accept the dental exams. The results for other psychological behavioral techniques were also inconsistent, including Treatment and Education of Autistic and related Communication-handicapped CHildren, Picture Exchange Communication System, Applied Behavior Analysis, video modeling, and distractions. Many of the studies were small and did not include a comparison group. They also did not consider factors like how severe the autism was, other conditions the children had, or their previous dental experiences. Because of these limitations, the evidence supporting the use of psychological behavioral techniques to improve dental visits for autistic children is limited and uncertain. More research with larger studies and proper control groups is needed to better understand this topic.

Structural Condition Assessment of Reinforced Concrete Bridge Using Operational Modal Analysis and Finite Element Model

The integration of Operational Modal Analysis (OMA) and Finite Element Model (FEM) techniques has proven to be a valuable approach for evaluating and maintaining the health of such structures. OMA extracts dynamic characteristics from a bridge's responses to ambient vibrations, while FEM employs computational models to simulate and compare these dynamic behaviours. This comprehensive methodology ensures an accurate representation of the bridge's behaviour and its correlation with real-world conditions. However, a significant challenge arises in assessing the Ultra High Performance Fiber Reinforced Concrete (UHPFRC) pedestrian bridge in Klang, Selangor, launched in October 2022, due to limited available information about OMA and FEM on UHPFRC. This research aims to develop FEM and conduct ambient vibration tests to obtain modal parameters. Moreover, the study investigates OMA techniques in the context of weak excitation sources, filling a critical knowledge gap. By comparing experimental results with FEM, this research provides insights into the feasibility and reliability of OMA under challenging environmental conditions. The principal results reveal significant percentage differences in natural frequencies between FEM and OMA, with the most notable disparities occurring in the first mode. Nevertheless, the mode shapes extracted from OMA closely resemble those from FEM. In conclusion, this research enhances the understanding of UHPFRC pedestrian bridge behaviour and modal analysis techniques.

Effectiveness of Differential Reinforcement and Least-to-Most Prompting in Reducing Non-compliance Behavior of Autistic Child

Background. Children with Autism Spectrum Disorder (ASD) are at higher risk of exhibiting challenging behaviors, and assessing their underlying functions is imperative in developing an individualized treatment for them. The present study aims to evaluate the effectiveness of the Applied Behavioral Analysis (ABA) technique to reduce active non-compliance behavior of a 6-year-old girl diagnosed with ASD in a private Special Education school setting. Method. The study was conducted in three different phases: (1) pre-intervention (baseline), (2) intervention, and (3) post-intervention phase, while using the partial interval recording method for collecting data. At baseline, the functional behavioral assessment (FBA) was employed to outline the function of active non-compliance behavior. During the intervention phase, the ABA technique of Differential reinforcement of low-rate behavior (DRL) and least-to-most prompting was implemented. Results. The result indicated a reduction in the problem behavior from 88.82% (baseline) to 38.85% during the intervention phase. Active non-compliance was estimated at 63.16% in the post-intervention period, which is comparatively exhibited at a lower rate. Also, the FBA suggested that active non-compliance behavior was maintained by escape/avoidance from the instructions. Conclusion. The study findings concludes that appropriate implementation of DRL arrangements and least-to-most prompting can effectively reduce non-compliant behaviors in ASD children. These data are further discussed, strategies to accommodate the child, and recommendations for future researches are also highlighted.

Naturalistic Developmental Behavioral Interventions for Developmental Language Disorder.

Naturalistic-developmental-behavioral interventions (NDBIs) are a widely accepted and validated approach for treating language-related symptoms in autism spectrum disorder, including deficits in vocabulary, social skills, and grammar. The purpose of this article is to define the elements naturalistic, developmental, and behavioral as applied to children with developmental language disorder (DLD) and to provide an example of how this type of intervention can be implemented to teach vocabulary and test cross-modal generalization between expressive and receptive modalities. A vocabulary intervention using hybrid NDBI methods (storybook reading and conversational recast interaction) was provided to three participants with DLD using a single-case design. The results indicated that all participants successfully learned receptive and expressive vocabulary targets with extensive cross-modal generalization. However, generalization was limited in some of the participants. This article provided definitions of key elements of NDBI (naturalistic, developmental, and behavioral) including a rationale for extending NDBI procedures to language intervention for children with DLD. In addition, a single-case design demonstrated that NDBI can be implemented in DLD and that broadly defined naturalistic-developmental applied behavior analysis techniques are applicable to language interventions for children with language disorders. Moreover, diverse naturalistic and developmental procedures meet the highest standards for evidence-based practice to treat DLD.

The Effect of Using Some Applied Behavior Analysis Techniques (Positive and Negative Reinforcement) in Teaching Practical Life Skills in Mentessori Approach for Children with Autism spectrum disorder (ASD)

The Effect of Using Some Applied Behavior Analysis Techniques (Positive and Negative Reinforcement) in Teaching Practical Life Skills in Mentessori Approach for Children with Autism spectrum disorder (ASD)

Feasibility, Contributions, and Challenges of Integrating Applied Behaviour Analysis Techniques into Mainstream Classrooms

Feasibility, Contributions, and Challenges of Integrating Applied Behaviour Analysis Techniques into Mainstream Classrooms

Non-consummatory behavior signals predict aversion-resistant alcohol drinking in head-fixed mice

A key facet of alcohol use disorder is continuing to drink alcohol despite negative consequences (so called “aversion-resistant drinking”). In this study, we sought to assess the degree to which head-fixed mice exhibit aversion-resistant drinking and to leverage behavioral analysis techniques available in head-fixture to relate non-consummatory behaviors to aversion-resistant drinking. We assessed aversion-resistant drinking in head-fixed female and male C57BL/6 J mice. We adulterated 20% (v/v) alcohol with varying concentrations of the bitter tastant quinine to measure the degree to which mice would continue to drink despite this aversive stimulus. We recorded high-resolution video of the mice during head-fixed drinking, tracked body parts with machine vision tools, and analyzed body movements in relation to consumption. Female and male head-fixed mice exhibited heterogenous levels of aversion-resistant drinking. Additionally, non-consummatory behaviors, such as paw movement and snout movement, were related to the intensity of aversion-resistant drinking. These studies demonstrate that head-fixed mice exhibit aversion-resistant drinking and that non-consummatory behaviors can be used to assess perceived aversiveness in this paradigm. Furthermore, these studies lay the groundwork for future experiments that will utilize advanced electrophysiological techniques to record from large populations of neurons during aversion-resistant drinking to understand the neurocomputational processes that drive this clinically relevant behavior.This article is part of the Special Issue on “PFC circuit function in psychiatric disease and relevant models”.

The histomorphological and stereological assessment of rat dorsal root ganglion tissues after various types of sciatic nerve injury.

Peripheral nerve injuries lead to significant changes in the dorsal root ganglia, where the cell bodies of the damaged axons are located. The sensory neurons and the surrounding satellite cells rearrange the composition of the intracellular organelles to enhance their plasticity for adaptation to changing conditions and response to injury. Meanwhile, satellite cells acquire phagocytic properties and work with macrophages to eliminate degenerated neurons. These structural and functional changes are not identical in all injury types. Understanding the cellular response, which varies according to the type of injury involved, is essential in determining the optimal method of treatment. In this research, we investigated the numerical and morphological changes in primary sensory neurons and satellite cells in the dorsal root ganglion 30days following chronic compression, crush, and transection injuries using stereology, high-resolution light microscopy, immunohistochemistry, and behavioral analysis techniques. Electron microscopic methods were employed to evaluate fine structural alterations in cells. Stereological evaluations revealed no statistically significant difference in terms of mean sensory neuron numbers (p > 0.05), although a significant decrease was observed in sensory neuron volumes in the transection and crush injury groups (p < 0.05). Active caspase-3 immunopositivity increased in the injury groups compared to the sham group (p < 0.05). While crush injury led to desensitization, chronic compression injury caused thermal hyperalgesia. Macrophage infiltrations were observed in all injury types. Electron microscopic results revealed that the chromatolysis response was triggered in the sensory neuron bodies from the transection injury group. An increase in organelle density was observed in the perikaryon of sensory neurons after crush-type injury. This indicates the presence of a more active regeneration process in crush-type injury than in other types. The effect of chronic compression injury is more devastating than that of crush-type injury, and the edema caused by compression significantly inhibits the regeneration process.

Potentiated GABAergic neuronal activities in the basolateral amygdala alleviate stress-induced depressive behaviors.

Major depressive disorder is a severe psychiatric disorder that afflicts ~17% of the world population. Neuroimaging investigations of depressed patients have consistently reported the dysfunction of the basolateral amygdala in the pathophysiology of depression. However, how the BLA and related circuits are implicated in the pathogenesis of depression is poorly understood. Here, we combined fiber photometry, immediate early gene expression (c-fos), optogenetics, chemogenetics, behavioral analysis, and viral tracing techniques to provide multiple lines of evidence of how the BLA neurons mediate depressive-like behavior. We demonstrated that the aversive stimuli elevated the neuronal activity of the excitatory BLA neurons (BLACAMKII neurons). Optogenetic activation of CAMKII neurons facilitates the induction of depressive-like behavior while inhibition of these neurons alleviates the depressive-like behavior. Next, we found that the chemogenetic inhibition of GABAergic neurons in the BLA (BLAGABA ) increased the firing frequency of CAMKII neurons and mediates the depressive-like phenotypes. Finally, through fiber photometry recording and chemogenetic manipulation, we proved that the activation of BLAGABA neurons inhibits BLACAMKII neuronal activity and alleviates depressive-like behavior in the mice. Thus, through evaluating BLAGABA and BLACAMKII neurons by distinct interaction, the BLA regulates depressive-like behavior.

Behavior Trees and State Machines in Robotics Applications

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Originally designed to model autonomous actors in computer games, behavior trees offer an extensible tree-based representation of missions and are claimed to support modular design and code reuse. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world. How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics: state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth. We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the community to use and further develop behavior trees, associated tools, and analysis techniques.

Комплексный подход в сопровождении ребенка с расстройством аутистического спектра на примере клинического случая

&lt;p style="text-align: justify;"&gt;&lt;strong&gt;Objectives. &lt;/strong&gt;The effects of the psycho-educational work in autism spectrum disorders (ASD) in preschool children is largely associated with timely comprehensive diagnostics. Present article aims to expound the possibilities of a mul&amp;shy;tidisciplinary diagnostical and psycho-educational approach using the analysis of a clinical case of a child with ASD as an example.&lt;/p&gt; &lt;p style="text-align: justify;"&gt;&lt;strong&gt;Methods&lt;/strong&gt;. Explored the clinical case of a 3 years 10 months old child with the registered ICD-10 DS F84.0 (Child&amp;shy;hood Autism). The child underwent a comprehensive developmental diagnostics and follow-up psycho-educational training sessions with a multidisciplinary team of specialists: a neuropsychologist, special teacher, speech therapist, ABA-therapist. Developmental diagnostics was attained using such methods as adapted Lurian neuropsychological tests, VB-MAPP and social skills assessment.&lt;/p&gt; &lt;p style="text-align: justify;"&gt;&lt;strong&gt;Results&lt;/strong&gt;. After the psycho-educational course child showed improvements in the following domains: communication, daily living skills, social interaction and cognitive functioning. Three factors influencing effects of work were identi&amp;shy;fied: a multidisciplinary approach to diagnosis and training; regularity and continuity of sessions until sustainable results are achieved (in case of severe disorders &amp;mdash; permanent work); using of various methods and tools for habilita&amp;shy;tion within the general psycho-educational framework.&lt;/p&gt; &lt;p style="text-align: justify;"&gt;&lt;strong&gt;Conclusions&lt;/strong&gt;. The complex diagnostics approach is relevant at all stages of psycho-educational work in order to indi&amp;shy;vidualize the programs used in accordance with the current level of development of the child. The authors suggest that the applied behavior analysis techniques can be integrated into the program of complex psycho-educational work.&lt;/p&gt;

Match analysis in wheelchair basketball: An observational analysis of the best team in the world (USA) in the 2020 Paralympic Games

This work shows the possibilities that observational analysis can offer to match analysis in Wheelchair Basketball, outlined in a study of the sequences that end in a shot, in the men's team considered to be the best in the world (USA), in the Tokyo Paralympic Games 2020. Data recording and coding the 527 sequences that end in a shot was carried out via the LINCE software, and inter-rater reliability guaranteed using Cohen's Kappa coefficient. Two diachronic behaviour analysis techniques – lag sequential analysis and T-pattern detection – were applied complementarily, thus allowing us to characterise effective and ineffective sequences. Effective behaviours are highlighted as being those which incorporate a final reception and shot by players with less functional limitation; or those in which the shot is made from the paint. Ineffective behaviours are those carried out by players with greater functional difficulty; or those which end in shots made from the offensive zone and external offensive zone.

Control of Web Traffic Using Web Application Firewall : Techniques and Challenges

The increasing complexity and sophistication of web-based applications have made them vulnerable to various security threats. Web Application Firewalls (WAFs) have emerged as an essential security measure to protect web applications from common attacks such as SQL injection, cross-site scripting (XSS), and others. This research paper explores the techniques and challenges involved in controlling web traffic using a Web Application Firewall. The paper discusses the working principles of WAFs, the various methods employed to analyze and filter web traffic, and the effectiveness of these techniques in mitigating different types of attacks. Additionally, the paper highlights the challenges faced in deploying and managing WAFs and provides insights into potential future developments in the field. This research paper delves into the techniques and challenges associated with controlling web traffic using a Web Application Firewall. It explores the fundamental principles of WAFs, including signature-based filtering, behavior-based analysis, whitelisting, blacklisting, rate limiting, and protection against common vulnerabilities. The paper evaluates the effectiveness of these techniques in mitigating various attacks and highlights the challenges involved in deploying and managing WAFs, such as configuration complexity, false positives and negatives, performance impact, and integration with existing security infrastructure. Furthermore, the paper discusses emerging trends and potential future directions in the field, including the integration of machine learning and artificial intelligence, advanced behavioral analysis techniques, and cloud-based architectures.

HUMAN FACTORS SECURITY ENGINEERING: THE FUTURE OF CYBERSECURITY TEAMS

ABSTRACT Human factors engineering has been a well-known practice since the early 1930s. This field of engineering is highly applicable and currently studied in relation to cybersecurity. Understanding the psychological components of human factors can help to resolve several concerns in the cybersecurity field. From employee recruitment and retention, to understanding burnout for SOC analysts, human factors are an emerging trend in cybersecurity. By researching and understanding the principles of human factors, cybersecurity professionals can improve security by design, reduce cognitive overload with security tooling, and improve awareness of behavioral analysis techniques. Human factors security has been increasingly gaining popularity in academic publications and will continue to trend upward as more researchers and technical professionals see the benefits of combining the two fields.

Mechanisms of Transmission and Processing of Pain: A Narrative Review

Knowledge about the mechanisms of transmission and the processing of nociceptive information, both in healthy and pathological states, has greatly expanded in recent years. This rapid progress is due to a multidisciplinary approach involving the simultaneous use of different branches of study, such as systems neurobiology, behavioral analysis, genetics, and cell and molecular techniques. This narrative review aims to clarify the mechanisms of transmission and the processing of pain while also taking into account the characteristics and properties of nociceptors and how the immune system influences pain perception. Moreover, several important aspects of this crucial theme of human life will be discussed. Nociceptor neurons and the immune system play a key role in pain and inflammation. The interactions between the immune system and nociceptors occur within peripheral sites of injury and the central nervous system. The modulation of nociceptor activity or chemical mediators may provide promising novel approaches to the treatment of pain and chronic inflammatory disease. The sensory nervous system is fundamental in the modulation of the host's protective response, and understanding its interactions is pivotal in the process of revealing new strategies for the treatment of pain.

An SUI-based approach to explore visual search results cluster-graphs.

Nowadays, exponential growth in online production and extensive perceptual power of visual contents (i.e., images) complicate the users' information needs. The research has shown that users are interested in satisfying their visual information needs by accessing the image objects. However, the exploration of images via existing search engines is challenging. Mainly, existing search engines employ linear lists or grid layouts, sorted in descending order of relevancy to the user's query to present the image results, which hinders image exploration via multiple information modalities associated with them. Furthermore, results at lower-ranking positions are cumbersome to reach. This research proposed a Search User Interface (SUI) approach to instantiate the non-linear reachability of the image results by enabling interactive exploration and visualization options. We represent the results in a cluster-graph data model, where the nodes represent images and the edges are multimodal similarity relationships. The results in clusters are reachable via multimodal similarity relationships. We instantiated the proposed approach over a real dataset of images and evaluated it via multiple types of usability tests and behavioral analysis techniques. The usability testing reveals good satisfaction (76.83%) and usability (83.73%) scores.