Behaviorism Research Articles

The role of blended learning in improving medical students’ academic performance: evidence from Pakistan

ObjectiveThe study examines the role of blended learning in improving medical students’ academic performance through self-regulatory learning and technological competence and identifies the moderating role of perceived institutional support in the relationships between self-regulatory learning, perceived teacher credibility, technological competencies, and academic performance.MethodsThe study was based on behavioral learning theory as a theoretical framework, and an adapted questionnaire was used to collect the data. In total, 275 medical students participated in the study, and the data was analyzed using structural equation modeling techniques with SmartPLS.ResultsThe results indicate that self-regulatory learning significantly affects student academic performance and mediates the role of teachers’ credibility and technological competencies. Furthermore, perceived institutional support is a significant moderator in the relationship between self-regulated learning, technological competencies, and teacher credibility.ConclusionThe study highlights the importance of self-regulated learning in students’ academic achievement. Moreover, it suggests that educational institutions should advance teachers’ competence and encourage collaborative learning to enhance students’ learning, motivation, and academic performance.

AI-Enhanced Cloud Security: Proactive Threat Detection and Response Mechanisms

AI-enhanced cloud security is revolutionizing the way organizations approach cybersecurity by leveraging advanced technologies like machine learning, natural language processing, and deep learning to proactively detect and respond to cyber threats. This paper explores the integration of AI into cloud security systems, highlighting its role in improving threat detection accuracy, reducing response times, and enhancing data protection across cloud environments. The paper also examines various AI-driven tools such as intrusion detection systems, behaviour analytics, and automated threat response mechanisms, which provide real-time defence capabilities. Despite its potential, the adoption of AI in cloud security faces challenges, including data privacy concerns, algorithmic bias, integration with legacy systems, and the high computational costs associated with AI models. Furthermore, ethical, and regulatory challenges complicate the deployment and scalability of AI in cloud security. By addressing these challenges and fostering continuous innovation, organizations can unlock the full potential of AI-enhanced security systems. This paper aims to provide a comprehensive overview of the current state of AI-driven cloud security, identify the key challenges faced by organizations, and offer insights into future developments and opportunities in this rapidly evolving field.

Detecting insider threats in banking using AI-driven anomaly detection with a data science approach to cybersecurity

The banking sector faces an increasingly critical challenge in detecting and preventing insider threats, which account for significant financial losses and data breaches annually. This comprehensive review explores how artificial intelligence-driven anomaly detection, integrated with advanced data science approaches and cybersecurity frameworks, is transforming insider threat detection in banking institutions. By synthesizing current research in behavioral analytics, machine learning methodologies, and employee activity monitoring, the study examines how AI-driven technologies are revolutionizing traditional approaches to insider threat detection and risk management. The review critically analyzes emerging AI-driven methodologies, particularly focusing on unsupervised learning techniques, behavioral pattern analysis, and real-time employee activity monitoring systems. Through an extensive examination of behavioral analytics frameworks, privileged access monitoring, and user entity behavior analytics (UEBA), the research illuminates both the potential and challenges of AI-powered insider threat detection. The investigation reveals significant advancements in behavioral anomaly detection, predictive modeling of employee activities and network behavior analysis while simultaneously addressing critical privacy considerations and regulatory complexities specific to employee monitoring.

Developing a data-driven predictive model for substance abuse prevention among youth using behavioral analytics

Substance abuse among youth presents a critical public health challenge, necessitating innovative approaches for early intervention and prevention. This study proposes the development of a data-driven predictive model that leverages behavioral analytics to identify youth at risk of substance abuse. The model utilizes data from multiple sources, including social media activity, school performance records, and mental health screenings, to analyze behavioral patterns that may indicate a predisposition toward substance misuse. The core methodology integrates machine learning algorithms to process and analyze large datasets, uncovering correlations between specific behaviors and the likelihood of substance abuse. Predictive features such as changes in social engagement, academic performance fluctuations, and indicators of emotional distress are identified and incorporated into the model to enhance its accuracy. By applying supervised learning techniques, the model is trained to recognize patterns in historical data, allowing it to make predictions about future substance use risks. Furthermore, the model's design emphasizes real-time monitoring and adaptability, enabling health professionals and educators to receive timely alerts and intervene early when behavioral warning signs are detected. The application of behavioral analytics in this context offers a more proactive, personalized approach to prevention, targeting at-risk individuals before they develop harmful substance use habits. In addition to its predictive capabilities, the model also provides actionable insights into effective intervention strategies. By identifying the most influential behavioral factors, it informs tailored prevention programs that address specific risk behaviors among youth. These findings can support policymakers and healthcare providers in developing data-driven, evidence-based prevention initiatives that better allocate resources to high-risk populations.

Deep Learning-Based Information Security

Abstract. With the rapid development of network technology, information security is facing increasingly complex challenges. Deep learning technology, due to its strong capabilities in data processing and pattern recognition, has become a key technology to improve the detection efficiency and accuracy in the field of information security. This paper delves into the application of deep learning in various aspects such as malware detection, network intrusion identification, User and Entity Behavior Analytics (UEBA), privacy protection technology, model explainability, and network security vulnerability detection, and proposes deep learning-based information security methods. Through experimental validation, our methods have outperformed traditional machine learning models in multiple evaluation metrics, providing new solutions for the field of information security. In the future, we will continue to explore new applications of deep learning in the field of information security to cope with the ever-changing network security threats.

Implementation of Basic Education Learning Continuity Plan at Anomar National High School, Surigao City, Philippines

This study assessed the implementation of the Basic Education Learning Continuity Plan (BE-LCP) at Anomar National High School, focusing on Grades 11 and 12 students' engagement with Most Essential Learning Competencies (MELCs) delivered via modular learning. Drawing on cognitive and behavioral learning theories, the study examined the roles of students’ interest and attitude, parental involvement, and teachers' support in shaping educational outcomes in a self-directed learning modality. Using a descriptive research design, data were collected through structured surveys and analyzed with statistical tools Simple Frequency Count and Percentages, Mean and Standard Deviation, Analysis of Variance, T-test, Pearson's R Correlation, and Multiple Regression Analysis. Findings indicated that while Grade 11 students achieved a "Satisfactory" level in learning assessment, Grade 12 outcomes were "Average," suggesting a need for additional support. High levels of interest and positive attitudes were noted among students, yet challenges with progressive skill development and external support highlighted areas for improvement. Parental involvement positively influenced learning environments but showed variability in effectiveness. The weak correlation between interest, attitude, and learning outcomes suggests that additional factors may impact student success. Recommendations include tailored interventions, enhanced parental guidance, and further exploration of curriculum adaptability and resource availability.

Social Engineering as a Major Cybersecurity Threat: Analysis of Challenges and Solutions for Organizations

Social engineering is a psychological manipulation technique used by attackers to exploit human weaknesses in information security. This research aims to identify the challenges organizations face in protecting themselves from social engineering attacks and offer effective solutions. Through analysis of case studies and relevant literature, it was found that a lack of employee awareness and training is one of the main causes of the success of these attacks. In addition, many organizations still rely on inadequate technology to detect threats. To address these issues, this paper recommends implementing regular training programs, strengthening security policies, and using advanced technology. With this comprehensive approach, organizations can strengthen their defences and reduce the risks associated with social engineering. Organizations should prioritize continuous education programs, foster a culture cantered on security, and establish protocols that encourage alertness. Moreover, robust access controls, defined incident reporting processes, and the use of technology like behavioural analytics can further reduce the risks posed by social engineering.

Developing Predictive Models for Financial Stability: Integrating Behavioral Analytics into Credit Risk Management

In this article, its author takes an attempt at investigating the use of behavioral analytics to complement credit risk management in the world of finance. Current mainstream credit risk models very much depend on conventional methods that incorporate historical financial information like credit scores and income while ignoring client behavioral contemporaneous patterns essential for modeling their risk levels. By this, the transactional patterns; payment behaviour and digital activities included besides the traditional financial parameters allows the financial institution to make superior models that gives better results on credit worthiness and risk identification earlier. It is crucial for both better risk assessment and for embracing more inclusion within lending networks and financial creativity. Analyzing the informational component of behavioral data as well as its applicability in credit risk modeling and considering the difficulties of implementing it in practice, the article provides an outline of the approaches to telemetry integration.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Study of Analysis of The Implementation of Behavioristic Learning Theory in Arabic Language Learning at Unity Theory with Direct Method for Beginner

This study analyzes the implementation of behavioristic learning theory in learning Arabic. It is an all-in-one method for beginners with direct methods. A literature study was used to collect qualitative data from literature relevant to behavioristic learning theory and direct methods in learning Arabic Unity theory. The study results indicate a strong suitability between Behavioristic theory and learning Arabic Unity theory. Behavioristic theory emphasizes the importance of repetition, reinforcement, and association, which are the core of direct methods in learning Arabic Unity theory. It is hoped that the results of this study can provide valuable contributions in developing learning strategies that can improve students' understanding of Arabic Unity theory.

Peningkatan Hasil Belajar dan Karakter Gotong Royong Siswa Kelas 2 SDN 07 Panjang

This study aims to evaluate the effectiveness of the application of Problem-Based Learning methods (Problem-Based Learning or PBL) in improving learning outcomes and mutual cooperation character of students in Grade 2 SDN 07 Panjang. The background of this study is based on The Independent curriculum that emphasizes strengthening the profile of Pancasila students, including the dimension of mutual assistance that is very relevant to social life in Indonesia. However, the implementation of The Independent curriculum in SDN 07 Panjang faces challenges, such as limited facilities and the need for training for teachers. These challenges can affect the formation of mutual cooperation characters in students as well as their academic learning outcomes. In this study, theories related to learning outcomes, Catholic Religious Education, and Problem-Based Learning Methods (PBL) are used as a basis for designing learning that can improve learning outcomes and mutual cooperation characters. Theoretical foundations such as Piaget's cognitive theory, Skinner's behavioristic theory, and Vygotsky's constructivism help to understand the mechanisms of effective learning. In addition, The Independent curriculum applied in Catholic Religious Education in Phase A supports the use of PBL to build student character, especially mutual aid. Through previous research, it was found that PBL was able to improve students ' social skills and learning outcomes. Studies by Nurhayati (2019) and Lestari (2017) show that the application of PBL in Catholic Religious Education subjects successfully improves understanding of religious teaching materials, student participation, and mutual cooperation characters. This study used the method of class action (CLA) which was carried out in two cycles. Each cycle includes a 105-minute opening, core, and closing activity. Subjects were 26 students of Grade 2 SD Phase A. Data were collected through observation and learning outcomes tests. The results showed that the application of PBL method significantly improved students ' cognitive learning outcomes and strengthened the character of mutual cooperation. In Cycle 2, there was an increase in student participation in group activities and understanding of the material.

Pilot Study: Behavioral and Medical Clinical Partnership for Optimization of Positive Airway Pressure Therapy in Pediatric Patients

BackgroundTo assess the effectiveness of a structured algorithm for pediatric positive airway pressure (PAP) initiation for the treatment of obstructive sleep apnea (OSA). MethodsAn algorithm was created to support pediatric PAP use and identify patients who could benefit from early behavioral consultation and education. Sleep providers implemented a unified introduction to PAP therapy with the PAP therapy toolkit. Through a collaborative approach with the medical equipment companies, pediatric patients were provided with PAP equipment before PAP titration studies and were allowed a gradual initiation of PAP therapy. PAP downloads were reviewed to determine improvements in compliance measured by percent of average days used and average minutes per night used. ResultsThirty-eight pediatric patients completed the PAP therapy algorithm. There was significant improvement in PAP compliance in percentage of days used (paired t test P value = 0.04), as well as an improvement (although not statistically significant) in average nightly use. Patients benefitted from close follow-up visits with the sleep advanced practice providers. Patients seen for the consultation with the pediatric behavioral psychologist showed statistically significant improvement in both percentage of days and average nightly use. ConclusionsOur pilot study shows that a behavioral and medical partnership using a structured algorithm was feasible and resulted in improved PAP compliance for our pediatric patients.

Adaptive Cybersecurity in the Digital Age: Emerging Threat Vectors and Next-Generation Defense Strategies

Abstract: This article examines the rapidly evolving landscape of cybersecurity, focusing on emerging threats and innovative defense mechanisms. We analyze four key threat vectors: Advanced Persistent Threats (APTs), ransomware, Internet of Things (IoT) vulnerabilities, and social engineering attacks. These threats pose significant risks to organizations, including data breaches, financial losses, and operational disruptions. In response, we explore cutting-edge defense mechanisms such as Artificial Intelligence and Machine Learning for threat detection, Zero Trust Architecture for access control, blockchain for data integrity, behavioral analytics for insider threat detection, and quantum cryptography for enhanced encryption. The article also outlines best practices for cybersecurity, including regular software updates, strong password policies, employee training, and security audits. By synthesizing current research and industry trends, this article provides a comprehensive overview of the cybersecurity landscape, emphasizing the need for continuous adaptation and vigilance in the face of evolving cyber threats

Alcohol Use Among American Youth: Differential Susceptibility to Peer Influence on Substance Use Tied to Genetic Risk

ABSTRACT The behavior of peers is one of the most robust correlates of deviance and arguably the strongest influence on substance use; however, not all youth mirror their peers’ behavior. Understanding how youth may be differentially susceptible to peer influence has implications for both theory and substance use policy. The potential moderation of the peer behavior-youth substance use relationship by genetic risk is explored to determine if differential susceptibility to peer influence is partially tied to biological factors. A subsample of data from the first two waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health) is utilized (n = 1,495). Evidence of a gene-environment interaction emerges whereby genetic risk conditions the relationship between peer substance use and early onset of alcohol use in models controlling for several risk and protective factors (e.g. poverty, self-control, victimization, social support, neighborhood dissatisfaction, religiosity). Genotype appears particularly salient when youth report a moderate degree of peer substance use (when peer substance use exceeds the mean but does not reach the extreme). This finding has implications for Burgess and Akers’ Social Learning Theory and other peer-based theories of behavior. It suggests that interventions to insulate youth from substance-using peers will be particularly impactful for those at enhanced genetic risk for early alcohol use. The identified gene-environment interaction partially explains why some youth are differentially susceptible to peer influence.

APPLICATION OF B.F. SKINNER'S BEHAVIORISM LEARNING THEORY IN ISLAMIC EDUCATION LEARNING FOR HIGH SCHOOL STUDENTS

This study aimed to know and describe the application of B.F Skinner's Operant Conditioning Behaviorism Learning Theory in Islamic Education learning at high school students and to know students' responses. In this study, the researchers described the forms of stimulus provision and the principles of Operant Conditioning B.F Skinner applied in Islamic Education learning. This research used qualitative methods. The data collection techniques used observation, interviews, and documentation. While data analysis is done with several stages, namely data reduction, data presentation, and conclusion drawing. The results showed that B.F Skinner's Operant Conditioning Behaviourism Learning Theory in Islamic Education learning was successfully applied. The implication of this research shows that overall the application of the theory resulted in a good response and changes in student behaviour in a positive direction in the form of the formation of learning motivation, activeness, reinforcement and improved student memory.

Application of Behaviorist Theory Based on the Learning Experienceof Students in Boarding School Environments

This study examines the importance of implementing behaviourist theory in student learning. It examines student responses and the role of the educational environment in implementing behaviourist theory at the Ar-Rifa'ie Tarakan Islamic Boarding School. The approach used in this study is qualitative, the type is descriptive and ethnographic. Data collection uses in-depth interviews and observations. The validity of the researcher's data uses source triangulation. The analysis adopts the Miles & Huberman model, which includes data collection, reduction, delivery, and conclusion. The study's results indicate that applying behaviourist learning theory uses reinforcement, motivation, incentives, and practice. Activities that utilize behaviourist learning theory include dhuha prayer, reading the Qur'an and tahfidz, Friday prayers, and muhadoroh (speaking practice). This is because the behaviourist or behavioural learning theory shows when students' behaviour changes in a positive direction, they become more motivated to learn and interact, and their memory and tolerance become stronger. The educational environment influences student behaviour and development, including climate, geographical location, habitat, and customs. This study only relies on behaviourist learning theory to explain changes in student behaviour. This may ignore other relevant educational approaches, such as cognitivism or constructivism theory, which can provide additional perspectives on developing students' learning. This study integrates behaviourist learning theory with students' religious activities, which is a unique approach because behaviourism is generally more often applied in the context of general education than religious education.

The Use of Big Data in Corporate Accounting and Data Analysis: Opportunities and Challenges

IntroductionThe era of Big Data technologies is restructuring corporate accounting, enabling a wide array of dynamic potential. This project explores how Big Data affects financial management, focusing on forecasting, risk management, and technological advances.MethodsThis work is informed by a large-scale review of scholarly literature, industry reports, and case studies. Databases like Google Scholar, PubMed, IEEE Xplore, Scopus, and Web of Science were used for data collection. Keywords included Big Data, corporate accounting, financial forecasting, risk management, data analytics, AI in accounting, machine learning in finance, and blockchain technology applied to accounting. The review was structured thematically, focusing on financial forecasting, risk management, and ethical considerations affected by Big Data practices in this domain.ResultsBig Data improves financial forecasting accuracy using historical data, market trends, and consumer behavior analytics. In risk management, Big Data facilitates effective proactive actions through thorough risk evaluation. Emerging technologies are anticipated to automate complex tasks, enhance predictive analytics, and improve the security and reliability of financial transactions.ConclusionsBig Data holds significant potential for corporate accounting, though challenges such as managerial complexity, data privacy, and expertise requirements for handling large volumes of data remain. The study highlights the importance of flexibility and technological adaptability, as well as specialized skill sets. It calls for continual dialogue and policy development to meet the ethical challenges presented by Big Data/AI, promoting responsible deployment while ensuring fairness. This review contributes to academic discourse and provides strategic guidance for practitioners in the evolving landscape of corporate accounting

Integrating Religious Moderation into Islamic Religious Education: Strategies and Impacts

This study examines the implementation of religious moderation values in Islamic religious education at SMK TI Bali Global Klungkung. Through a qualitative approach, the main finding of this research is the identification of ten religious moderation values being implemented: justice, balance, tolerance, centrism, love for the country, deliberation, improvement, pioneering, non-violence, and cultural friendliness. The implementation is achieved by integrating these values into the curriculum, evaluation techniques, and daily life practices, as well as through habituation and exemplification. The results have positive implications, such as establishing harmonious relationships between Muslim students and the community, increasing student achievement and creativity, and boosting the number of Muslim students in the school. The findings align with the concept of moderation in Islam, behavioristic theory, and expert opinions on religious moderation as a solution to the problem of radicalism. This research contributes to forming students with moderate, tolerant, fair, balanced, and non-violent characters who can adapt to a pluralistic environment following the objectives of Islamic education. The limitation of this study is that its scope is confined to one school.

Integrating Behavioral Analytics and Intrusion Detection Systems to Protect Critical Infrastructure and Smart Cities

In an age notable by growing digitization and relatedness, protecting critical infrastructure and smart cities against cyber threats is a biggest obstacle. This abstract examines the combination of Behavioural Analytics (BA) and Intrusion Detection Systems (IDS) as a active and best plan to boost cybersecurity defences. Behavioural Analytics uses machine learning algorithms and statistical models to notice usual entities behaviour patterns inside networks, empowering the identification of anomalies that indicate possible security infringements. This approach is improved through modern techniques that includes Statistical Anomaly Detection, which measures divergence and Long Short-Term Memory (LSTM) networks, skilled at grabbing temporal dependencies in data flow of network pursuit. Cross-Event Correlation methodologies and approaches improve the abilities of IDS by finding similarity between disparate events, giving a broad aspect of possible threats across inter related systems. Entity Behaviour Analysis (EBA) enhance these works by building thorough behaviour profiles and allocating risk scores based on divergence, improving targeted response plans. Network-Based IDS (NIDS) lengthen defence by observing whole networks for unusual activities, while Cyber Threat Intelligence (CTI) devices gives findings into progressing threats, enabling defensive security scales. Convolutional Neural Networks (CNNs) plays a part in removing complicated attribute from network data, improving anomaly detection. The results shows enhancements in threat detection accuracy, with a drop in false positives by 30% and an rise in anomaly detection precision to 95%. The Sensor Data (Units) changes from 80 to 90 units over monitored time periods.

The Influence of Mental Health Education on Adolescents Knowledge in Preventing Bullying Based on Behavioristic Theory

Adolescence is a period that is vulnerable to experiencing negative behavior in friendships, especially at school. One of the bad behaviors that often occurs during adolescence is bullying. Bullying is a violent activity that has a negative impact, especially on mental health. To overcome the problem of bullying among teenagers, education is needed about the impact of bullying on mental health, using a behaviorist theory approach. Therefore, this research aims to determine the effect of mental health education on teenagers' knowledge of preventing bullying using a behaviorist theory approach. This research uses quantitative methods with a quasi-experimental approach and a one-group pretest-posttest design. The results of this study show that there is a significant influence of mental health educational interventions on teenagers' knowledge of preventing bullying.