Behavioral Model Research Articles

A Behavioral Screening Method for Predicting PTSD-like Phenotypes: Novel Application to Female Rats.

Only a small percentage of trauma-exposed subjects develop PTSD, with females being twice as likely. Most rodent models focus on males and fail to account for inter-individual variability in females. We tested a behavioral PTSD model in female rats to distinguish between susceptible and resilient individuals. In Experiment 1, female rats underwent footshocks paired with social isolation, a PTSD risk factor. They were re-exposed to the conditioned context to test memory retention, and assessed in the Elevated Plus Maze (EPM) and Social Interaction (SI) tests for anxiety and social behavior. Footshock-exposed rats showed fear memory retention up to 16 days, indicated by elevated freezing behavior during re-exposure. They also exhibited reduced exploration in the EPM and less SI time compared to controls. In Experiment 2, we classified rats into normal responders, susceptible, and resilient groups based on locomotor activity after trauma, correlating with memory retention and anxiety. Unlike existing models focused on males and lacking predictive variables before trauma, our method identifies PTSD-like susceptibility and resilience in female rats by using exploratory behavior as a predictor before trauma exposure. Exploratory activity in a novel environment after trauma and before extinction is a reliable predictor of PTSD-like phenotypes and differentiates between susceptible and resilient female rats.

A Machine Learning-Based Risk Assessment Study of Unsafe Behavior of Workers in Nuclear Power Plants Under Construction

Most accidents during the construction of nuclear power plants are caused by human unsafe behavior. How to scientifically determine the risk management priority of human unsafe behaviors is the basis for effectively preventing accidents in under-construction nuclear power plants. Although employees are adopted for control in under-construction nuclear power plants, the records of unsafe behaviors are mostly recorded by inspectors, and the records of behaviors may have missing values. To overcome the above problems, this paper applies machine learning algorithms to construct an employee behavioral risk assessment model. Firstly, by analyzing the influencing factors of unsafe behaviors, the assessment indexes are proposed, then the Random Forest algorithm is used to obtain the characteristic importance of the proposed indexes and exclude those with smaller characteristic importance. Finally, the harmony search (HS) algorithm is used to optimize the back propagation (BP) neural network to construct an assessment model and compare with the BP evaluation model. The results show that the HS-BP model is more accurate and efficient. The results show that the method can comprehensively and effectively analyze workers‘ unsafe behaviors, and the BP neural network is optimized to construct the assessment model using the Harmonic Search algorithm, which is more accurate than the original model. The use of the machine learning method to assess workers’ behaviors can objectively output the risk level and overcome the one-sidedness and subjectivity of the traditional expert evaluation method.

Automatic Extraction and Compensation of P-Bit Device Variations in Large Array Utilizing Boltzmann Machine Training

A Probabilistic Bit (P-Bit) device serves as the core hardware for implementing Ising computation. However, the severe intrinsic variations of stochastic P-Bit devices hinder the large-scale expansion of the P-Bit array, significantly limiting the practical usage of Ising computation. In this work, a behavioral model which attributes P-Bit variations to two parameters, α and ΔV, is proposed. Then the weight compensation method is introduced, which can mitigate α and ΔV of P-Bit device variations by rederiving the weight matrix, enabling them to compute as ideal identical P-Bits without the need for weights retraining. Accurately extracting the α and ΔV simultaneously from a large P-Bit array which is prerequisite for the weight compensation method is a crucial and challenging task. To solve this obstacle, we present the novel automatic variation extraction algorithm which can extract device variations of each P-Bit in a large array based on Boltzmann machine learning. In order for the accurate extraction of variations from an extendable P-Bit array, an Ising Hamiltonian based on a 3D ferromagnetic model is constructed, achieving precise and scalable array variation extraction. The proposed Automatic Extraction and Compensation algorithm is utilized to solve both 16-city traveling salesman problem (TSP) and 21-bit integer factorization on a large P-Bit array with variation, demonstrating its accuracy, transferability, and scalability.

Low-impact, near real-time risk assessment for legacy IT infrastructures

In an era where cybersecurity threats are evolving at an unprecedented pace, this paper introduces a methodology for near real-time risk assessment of high-profile, high security infrastructures, where data security and operational continuity inherently limits observability. Our approach addresses the challenges of this limited observability and minimized disruption, offering a new perspective on processing and evaluating cybersecurity knowledge. We present an innovative method that leverages attack graphs and attacker behavior analysis to assess risks and vulnerabilities. Our research includes the development of an automated risk assessment mechanism, graphical security modeling, and a Markov chain-based model for attacker behavior. Our methodology utilizes a blend of direct and indirect event sources, incorporating an attacker behavioral model based on a random walk method akin to Google’s PageRank. The proof-of-concept solution calculates potential risk according to the actual threat landscape, providing a more accurate and timely assessment.

Agent-Based Behavioral Modeling of Human Associative Learning in a Complex Approach-Avoidance Conflict Task

Abstract Despite its key role in the development, maintenance, and treatment of anxiety disorders, the detailed mechanisms of human avoidance learning remain elusive. To contribute to the understanding of avoidance learning, we here report on a novel approach-avoidance conflict task that requires participants to learn associations between complex visual stimuli and combined appetitive and aversive stimuli while actively engaging with the experimental environment. Using an agent-based behavioral modeling approach, we implemented and validated an extensive set of control, heuristic, Rescorla-Wagner learning-based, and hybrid agents. We show that a Rescorla-Wagner learning-based agent with a prior expectation bias parameter best explains the learning behavior of 50 participants. As such, our work complements current research on the computational underpinnings of approach-avoidance behavior by showing paradigm and task instruction dependencies in approach-avoidance-relevant associative learning and contributes to the overall aim of achieving a more fine-grained understanding of the etiology of anxiety disorders.

Vibrio cholerae Gut Colonization of Zebrafish Larvae Induces a Dampened Sensorimotor Response.

Background: Cholera is a diarrheal disease prevalent in populations without access to clean water. Cholera is caused by Vibrio cholerae, which colonizes the upper small intestine in humans once ingested. A growing number of studies suggest that the gut microbiome composition modulates animal behavior. Zebrafish are an established cholera model that can maintain a complex, mature gut microbiome during infection. Larval zebrafish, which have immature gut microbiomes, provide the advantage of high-throughput analyses for established behavioral models. Methods: We identified the effects of V. cholerae O1 El Tor C6706 colonization at 5 days post-fertilization (dpf) on larval zebrafish behavior by tracking startle responses at 10 dpf. We also characterized the larval gut microbiome using 16S rRNA sequencing. V. cholerae-infected or uninfected control groups were exposed to either an alternating light/dark stimuli or a single-tap stimulus, and average distance and velocity were tracked. Results: While there was no significant difference in the light/dark trial, we report a significant decrease in distance moved for C6706-colonized larvae during the single-tap trial. Conclusion: This suggests that early V. cholerae colonization of the larval gut microbiome has a dampening effect on sensorimotor function, supporting the idea of a link between the gut microbiome and behavior.

Effects of a Behavior Intervention Based on the Recurrence Risk Perception and Behavioral Decision Model for Ischemic Stroke Patients: A Randomized Controlled Trial.

Behavioral management is essential to preventing recurrence after stroke, but its adherence is limited worldwide. We aimed to assess the impact of the behavior intervention based on the Recurrence risk perception and Behavioral decision Model for ischemic stroke patients' health behavior. This study was a single-blind, randomized, controlled trial with a 3-month follow-up. The outcome measures were the perception of the risk of stroke recurrence, behavioral decision, and health behavior. A total of seventy participants were randomized to the intervention group (n = 35) or control group (n = 35). The former received a twelve-week theory-based intervention in addition to the routine education, while the control group received only the routine education. The generalized estimating equations results indicated that the intervention group had significantly greater improvements in perception of the risk of stroke recurrence compared to the control group at all T1 (B = 0.13, 95% CI: 0.03 to 0.23), T2 (B = 0.18, 95% CI: 0.07 to 0.28), and T3 (B = 0.16, 95% CI: 0.07 to 0.25) after adjusting for stroke frequency. Statistically significant improvements were found in behavioral decision for the intervention group compared with the control group at T2 (B = 0.25, 95% CI: 0.09 to 0.41) and T3 (B = 0.26, 95% CI: 0.10 to 0.43). Results also showed a significantly higher increase in health behavior at T1 (B = 0.29, 95% CI: 0.09 to 0.48) and T2 (B = 0.22, 95% CI: 0.04 to 0.40). The intervention can improve the perception of the risk of stroke recurrence, behavioral decision, and health behavior in ischemic stroke patients. IMPLICATIONS: This study provides a reference point for promoting healthy behaviors in patients with ischemic stroke. A recurrence risk perception and behavioral decision model-based intervention was deemed to be feasible and useful in practice. PATIENT OR PUBLIC CONTRIBUTION: Patients and their caregivers agreed to participate in the study and shared their experiences of participating in research with us.

Sharing economy as an alternative to fast fashion: a behavioral intention model

Purpose This study aims to investigate the factors influencing the adoption of sharing economy (SE) practices, specifically focusing on virtual clothing sharing platforms (VCSPs) as a sustainable alternative to fast fashion (FF). Given the negative environmental impact, labor exploitation and disposable culture associated with FF, this research aims to understand how economic, environmental and social motivations affect consumer behavioral intentions toward using VCSPs. Design/methodology/approach Data were collected through a survey of 392 participants from a global online academic institution operating across Europe, Asia, America and Africa. The sample included individuals from diverse professional, demographic and educational backgrounds, providing relevant context for studying VCSPs. The study uses the decomposed theory of planned behavior (DTPB) and structural equation modeling to analyze the data, focusing on how motivations and COVID-19 influence consumer engagement with VCSPs. Findings The results reveal that environmental and prosocial motivations are the primary determinants driving VCSPs adoption, followed by economic and social factors. The study also identifies a significant impact of COVID-19 on consumer intentions. Originality/value This research expands the literature on SE within the fashion sector by providing new insights into consumer behavior. The proposed model enhances the predictive power of DTPB by integrating additional variables, including trust, decomposing perceived utility into four dimensions, COVID-19 influence and new relationships, offering a more holistic view of the adoption process. The findings have significant implications for advancing sustainable fashion initiatives and offer viable alternatives to FF.

“We don’t Get Drugs Targeted for Us:” Applying the Integrated Behavioral Model to Understand Why Black Women Chose to Participate in a Breast Cancer Clinical Trial

ABSTRACT Guided by the integrated behavioral model, the authors interviewed 14 Black breast cancer survivors (N = 14) who had participated in a breast cancer clinical trial. This study aimed to better understand what may motivate Black women to engage in medical research and decide to participate in medical research. Findings revealed that Black women’s altruistic desires to serve others and their communities are greatly influenced by the need to leave a “legacy” of better treatment for other Black women. The participants mostly learned about clinical trials through communicating with friends, family, or other breast cancer patients and survivors, rather than from their physicians. Many were influenced to participate by other Black breast cancer patients they knew, suggesting that social norms messaging may help alert other Black women about the continuing disparity in clinical trial participation. Finally, the participants in this study demonstrated high levels of involvement not only in seeking out clinical trials, but also in engaging in informed and shared decision-making with their providers about participating in the trials. The findings from this work illuminate important reasons Black women chose to participate in breast cancer clinical trials. Additionally, we offer robust and valuable theoretical and practical implications for researchers, so they can work toward successfully increasing Black women’s participation in clinical trials.

ІНФОРМАЦІЙНИЙ ШУМ ТА МАРКЕТИНГОВІ ІНСТРУМЕНТИ ДЛЯ ЙОГО ПОДОЛАННЯ

Introduction. In the modern conditions of the formation of the digital economy and the intensive growth of the volume of information to be processed by analysts, fundamentally new requirements are being formed for consumers and businesses. At the same time, data processing tools do not always provide adequate analytical information regarding changes occurring both in the global and local markets. The reason for this is information noise, which is an excessive flow of data that complicates the perception of important messages and affects the effectiveness of marketing strategies. All this requires the formation of a systematic approach to managing information flows and optimizing communications between the enterprise and the target audience. The purpose of the article. The purpose of the study is to analyze information noise as a barrier to the effective perception of marketing communications, as well as to identify tools and strategies that allow minimizing its impact on consumers and increasing the effectiveness of marketing activities. Methods. The research is based on the use of an interdisciplinary approach, which includes content analysis, abstract modeling methods for studying consumer perception of information, as well as empirical methods for studying the effectiveness of modern marketing technologies, based on the analysis of approaches to managing specialized marketing tools for interacting with consumers. Results. The article considers the conceptual aspects of information noise and its functional impact on consumer behavior. The effectiveness of using various marketing tools based on the automation of business processes, the use of communication platforms and data for analyzing consumer behavior is analyzed, which results in increasing the efficiency of communications and reducing noise effects. Key strategies for combating information noise are identified, which contribute to strengthening the communication interaction of the enterprise in the context of dynamic changes in market conditions. The need for implementing innovative approaches to developing advertising campaigns that take into account the specifics of modern media channels and consumer behavioral models is proven. Conclusions. It has been proven that minimizing information noise is an important factor in ensuring the long-term effectiveness of a company's marketing communications. It has been determined that the use of modern data analysis tools, process automation, and an individual approach to the consumer allows a company to increase the level of trust and loyalty from customers, which results in an increase in sales and profitability of the business entity.

Modelling of online shopping behavior in the Czech online environment.

The online environment has its own specifics, which shape the specific behavior of all market subjects, both customers and companies that trade electronically. The aim of the paper is to create, quantify and verify a conceptual comprehensive model of relationships between determinants that influence consumers when shopping online. The impetus for the conducted research was the discovery of the non-existence of a comprehensive model of online shopping behavior that reflects the specifics of the online environment. The main research method is the method of online questioning in the form of a questionnaire survey among a selected group of Czech respondents (n = 926) shopping online with the aim of evaluating the determinants of online shopping behavior. The results of the questionnaire survey are subsequently used to build a comprehensive model of online shopping behavior, which was statistically compiled and verified using the PLS-SEM method, which, based on statistical data, estimates the size and quality of the links between the measured (manifest) and assumed unmeasured (latent) variables. The results show that the selected factors (31 factors) explain up to 82.53% of the variability of the total variance. The results of the correlation analysis of the factors confirmed that the defined factors are not mutually dependent and that in the comprehensive model the factors are not only identified but also statistically significant. The results also confirmed that the correlation of e.g. psychological factors are stronger than dependence on other investigated factors in the comprehensive model of online shopping behavior. The research clearly showed that the key factors for customers when shopping online are Security and risk elimination (SE), together with the Online distribution and logistics (OD) and Online payments (OP). Impulsive online shopping was identified by customers as the least important factor. The validated model provides a comprehensive explanation of the current phenomenon of online shopping that integrates and extends previous studies identifying behavioral models of online shopping behavior.

Analysis of financial literacy and inclusion factors in Peruvian university women

Women’s financial literacy and financial inclusion have gained prominence in recent years. Despite progress, knowledge and access to finance remain common barriers for women, especially in emerging economies. Globally, domestic and economic violence has been recognized as a relevant social concern from a gender perspective. In this context, financial literacy and financial inclusion are considered to play a key role in reducing violence against women by empowering them with the necessary knowledge to manage their financial resources and make informed decisions. This study aims to evaluate the determinants that influence Peruvian female university students’ financial literacy and financial inclusion. To this end, a theoretical behavioral model is proposed, and a survey is applied to 427 female university students. The results are analyzed using a Partial Least Squares Structural Equation Model (PLS-SEM). The results validate all the proposed hypotheses and highlight significant relationships between financial literacy and women’s financial inclusion. A relevant relationship between financial attitude and financial behavior is also observed, as well as the influence of financial behavior and financial self-efficacy on financial literacy. The results also reveal that women feel capable of making important financial decisions for themselves and consider that financial literacy could help reduce gender-based violence. Based on these findings, theoretical and practical implications are raised. It highlights the proposal of a theoretical model based on antecedents, statistically validated in a sample of women in Peru, which lays the foundation for understanding financial literacy and financial inclusion in the Latin American region.

Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models.

We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp library for Python. ActiveInference.jl is compatible with cutting-edge Julia libraries designed for cognitive and behavioural modelling, as it is used in computational psychiatry, cognitive science and neuroscience. This means that POMDP active inference models can now be easily fit to empirically observed behaviour using sampling, as well as variational methods. In this article, we show how ActiveInference.jl makes building POMDP active inference models straightforward, and how it enables researchers to use them for simulation, as well as fitting them to data or performing a model comparison.

Factors associated with preventive health services use by older war-displaced Japanese returning from China and by older Korean residents of Japan: a descriptive correlational study

BackgroundAmong the people with diverse backgrounds and cultural customs living in Japan, two important groups, namely, war-displaced Japanese returning from China and South and North Korean nationals who are naturalized citizens residing in Japan, will experience population aging in the same way as the general Japanese population. In old age, physical function generally declines, multiple diseases are more likely to occur, and health issues that need to be addressed increase in number. The aim of this study was to identify the factors associated with the use of preventive health services in Japan by older Korean residents and war-displaced Japanese returning from China.MethodsThe study conducted a cross-sectional study in Japan using a self-administered questionnaire. Respondents comprised 754 people aged 65 or over, including 220 Japanese, 200 war-displaced Japanese returnees from China, and 334 Korean residents of Japan. The behavioral model of health services use served as the analytical framework. Questionnaire items were based on predisposing factors, enabling factors, and need factors. Logistic regression analysis was used to examine the relationship between preventive health services use and each factor.ResultsThe results of the logistic regression analysis showed that being ethnically a war-displaced Japanese returning from China (OR = 0.04; 95% CI [0.00, 0.74]) and being ethnically Korean residents of Japan (OR = 0.13; 95% CI [0.03, 0.62]) were associated with the use of preventive health services.ConclusionsThe use of preventive health services by older people in Japan is influenced by ethnicity. For these reasons, war-displaced Japanese returning from China and Korean residents of Japan need support to encourage their greater use of preventive health services.

Factors affecting the utilization of continuum of maternal healthcare services: Evidence from rural areas using Andersen-Newman behavioral model

Factors affecting the utilization of continuum of maternal healthcare services: Evidence from rural areas using Andersen-Newman behavioral model

Determinants of healthcare utilization under the Indonesian national health insurance system – a cross-sectional study

BackgroundIndonesia has implemented a series of healthcare reforms including its national health insurance scheme (Jaminan Kesehatan Nasional, JKN) to achieve universal health coverage. However, there is evidence of inequitable healthcare utilization in Indonesia, raising concerns that the poor might not be benefiting fully from government subsidies. This study aims to identify factors affecting healthcare utilization in Indonesia.MethodsThis study analysed cross-sectional survey data collected by the “Equity and Health Care Financing in Indonesia” (ENHANCE) Study. Andersen’s behavioural model of health services use was adopted as a framework for understanding healthcare utilization in Indonesia. Sociodemographic variables were categorized into predisposing, enabling and need factors. Outcome measures included the utilization of primary and secondary health services. Multi-level logistic regression models were run to examine factors associated with each type of health service utilization.ResultsOf the 31,864 individuals included in the ENHANCE survey, around 14% had used outpatient services in the past month. Fewer than 5% of the study population had visited hospitals for inpatient care and about 23% used maternal and child health services in the past 12 months. Age, gender and self-rated health were key determinants of health services utilization. No significant differences in primary care utilization were found among people with different insurance status, but people who received subsidised premiums under the JKN were more likely to receive primary care from public health facilities and less likely from private health facilities. Compared to people who pay JKN insurance premiums themselves, the uninsured and those whose premiums were subsidised by the government were less likely to visit public and private hospitals when other factors were controlled.ConclusionThis study demonstrates that the distribution of healthcare utilization in Indonesia is largely equitable as predisposing factors (age and gender) and health need were found to greatly influence the utilization of different types of health services. However, enabling factors such as health insurance status were also found to be associated with inequity in utilization of hospital services. Further policy actions regarding resource allocation and health service planning are warranted to achieve a more equitable pattern of health service use in Indonesia.

Utilization of Anal Cytology Screening among Sexual and Gender Minority Populations in Pennsylvania.

Despite the risk of anal cancer in sexual and gender minority (SGM) populations, anal cancer screening remains infrequent and inconsistent in these populations. The objective of this analysis was to identify factors associated with anal cancer screenings among SGM populations using the Andersen's behavioral model of health services use. Secondary analyses of two cross-sectional surveys from the 2020 (N = 1,125) and 2022 (N = 630) Pennsylvania LGBTQ Health Needs Assessment. Multiple logistic regression analyses were used to identify correlates of anal cytology screening. Average age was 37.7 (SD = 13.3) and 39.7 (SD = 13.6) in 2020 and 2022, respectively. Approximately 16% to 18% reported living with human immunodeficiency virus (HIV). A minority of respondents reported past year screening (14.0%, 2020 and 13.6%, 2022). Enabling and need-based factors consistently associated with screening included sexually transmitted infection treatment, living with HIV, preexposure prophylaxis use, and multiple sex partners. Robust factors associated with ever being screened were age and living with HIV. Anal cytology screening is being done in Pennsylvania at a small but not insignificant rate. In accordance with existing guidelines, SGM populations living with HIV were most likely to be screened, but still at a low rate. Predictive factors associated with screening in this study can inform future interventions to implement guideline-specific anal cancer prevention. Factors that reflect consistent connection to healthcare are associated with increased rates of screening via anal cytology testing, indicating there are opportunities to implement anal cancer screening as part of a larger, more comprehensive SGM-focused care pathway.

Building Wealth, Building Health: Asset Holding and Health Care Utilization Among U.S. Immigrants.

Immigrants' low socioeconomic status is consistently linked to reduced healthcare utilization. This study extends the Andersen Behavioral Model by incorporating asset ownership as an enabling factor and examining its role across immigrant origin groups. It addresses two questions: (1) What is the relationship between asset ownership and healthcare utilization among immigrants? (2) Are there ethnic differences in this relationship? Data from 4730 adults in the National Immigrant Survey (NIS) were analyzed. Missing income values were handled using multiple imputations. Healthcare utilization was measured as physician and dentist visits (dichotomous variables). Independent variables included demographic, immigration, health, and asset factors. Logistic regression was used to examine the impact of financial and real assets on healthcare utilization. Asset ownership significantly enhances healthcare utilization among immigrants. Individuals with financial assets were 124% more likely to visit a physician and 66% more likely to visit a dentist, while real assets increased these probabilities by 35% and 22%, respectively. Sub-ethnic analyses showed that both asset types positively influenced physician visits for Asians and Europeans, but not Africans. For dentist visits, only Europeans showed significant associations with asset ownership, while Middle Easterners and Africans did not. These findings support the inclusion of asset ownership in the Andersen model, highlighting its relevance for immigrant populations. Asset-building programs could enhance healthcare utilization and improve immigrant health outcomes. Tailored policies are recommended to address the unique needs of diverse ethnic groups.

The development and validation of the Student Self-feedback Behavior Scale.

Though the importance and benefits of students' active role in the feedback process have been widely discussed in the literature, an instrument for measuring students' self-feedback behavior is still lacking. This paper reports the development and validation of the Self-feedback Behavior Scale (SfBS), which comprises three dimensions (seeking, processing, and using feedback). The SfBS items were constructed in line with the self-feedback behavioral model. One thousand two hundred fifty-two high school students (Grade 10 to Grade 12) in mainland China participated in this survey. The exploratory factor analysis revealed a three-factor model reaffirmed in the confirmatory factor analysis. The multi-group CFA supported the measurement invariance of the SfBS across gender. Using the SfBS can help researchers and teachers better understand students' self-feedback behavior and optimize benefits derived from the self-feedback process.

A Novel Behavioral Monitoring based Trust Model for enhancing Edge Security using Adaptive Neuro-fuzzy Inference System

The Internet of Things (IoT) is in a recent state of instability due to the flooding of virtual data. It is believed that IoT and cloud computing have met their maximum thresholds and loading them with data after this point will only deteriorate their performance. Hence, edge computing has been introduced to mitigate the processing burden of IoT. To meet the security demands of edge computing, we intend to combine the method of blockchain along with edge computing for a better solution. Accordingly, this paper proposes the introduction of a novel blockchain model that is based on artificial neural networks and trust estimation called the behavioral monitoring trust estimation model. Performance metrics such as accuracy, precision, recall, and F-measure are calculated under normal conditions and under the injection of attacks like false data injection, booting attack, and node capturing. The proposed behavioral monitoring trust classification model is compared with existing classifiers like Naive Bayes, K-nearest neighbor, Auto Encoder, Random Forest, and Support Vector Machine, and is found to have improved performance. Additional evaluation parameters like execution time, encryption time, storage cost, computational overhead, energy efficiency, and packet drop possibility are also calculated for the proposed model and compared with existing blockchain techniques of Bitcoin, Ethereum, Hyperledger, Direct and indirect trust model, and mutual trust chain based blockchain model. The proposed model achieved an accuracy of 95%, a precision score of 90%, a recall score of 94%, and an F-measure of 94% indicating superior performance.