Journal of Information Systems Engineering and Management
Journal of Information Systems Engineering and Management Journal of Information Systems Engineering & Management is published quarterly ISSN (Online): 2468-4376 DOI: https://doi.org/10.55267/iadt
Assessment of Water, Sanitation, and Hygiene [WASH] in Health Care Facilities within Schistosomiasis Endemic Districts in Ghana
Globally, Schistosomiasis remains a significant public health problem and burden in the tropical regions which affect millions of people. Poor water, sanitation and hygiene [WASH] services and infrastructure increases the risk of occurrence of Schistosomiasis. Although there are existing studies on schistosomiasis prevalence and risk factors in Ghana, there is a scarcity of information regarding WASH services specifically within healthcare facilities in highly endemic regions. This study aimed to assess WASH in healthcare facilities across schistosomiasis endemic districts in Ghana's Oti and Upper East regions. The study assessed WASH services and infrastructure in all 197 healthcare facilities in Ghana's Oti and Upper East regions using a quantitative study design. The research employed the WHO/UNICEF Water and Sanitation for Health Facility Improvement Tools [WASH FIT] for data collection from healthcare workers and analysed using STATA version 16. The study focused on districts with high schistosomiasis endemicity. The ethical approval was received from the Navrongo Health Research Centre, Institutional Review Board [NHRCIRB] and appropriate consent was sorted from all health facilities heads and Ghana Health Service. WHO/UNICEF WASH FIT evaluation tool revealed critical deficiencies in 197 healthcare facilities, including 22.3% lacking improved water sources, 66.7% better water supply, and 67% low water availability and sufficiency. Additionally, sanitation facilities faced challenges, with 14% improved toilets, and 33.33% less usable toilets. The study highlights the gaps in WASH in health care facilities which can result in increased transmission of schistosomiasis. This implies the need for policy interventions to improve healthcare WASH services through targeted investments, infrastructure enhancements, and comprehensive programs, while also recommending further longitudinal studies.
Copyright (c) 2025 Journal of Information Systems Engineering and Management
Data-Driven Automation for Operational Efficiency in Enterprise Payments
This research article evaluates a novel model designed to enhance system performance, reliability, and scalability across diverse operational environments. The primary objective is to assess the model’s ability to reduce processing time, minimize error rates, and optimize resource utilization more effectively than previously applied approaches. The study also aims to demonstrate how such improvements contribute to long-term operational resilience and strategic cost efficiency. Quantitative results showed significant improvements across all key performance areas: processing time was reduced by up to 30%, error rates were halved, and resource efficiency improved by nearly 40%. These gains were observed across development, testing, staging, and production environments, validating the framework’s robustness. The framework was further analyzed using statistical tools and visual methods, including swap layouts, Monte Carlo simulations, and radar charts—to evaluate its long-term scalability. It demonstrated the ability to scale without proportional cost increases, making it suitable for enterprise-wide deployment. The findings indicate that the proposed system framework delivers substantial business value by minimizing downtime, reducing operational costs, and enhancing user experience. It is technically viable for deployment in large-scale enterprise systems and serves as a competitive alternative to existing solutions.
Technovation Access Tools and Information Resources Accessibility in Libraries
This study examined the role of technovation access tools in information resources accessibility in libraries. The 21st century has ushered in a drastic shift in information resources accessibility, driven largely by the deployment of technovation access tools. This study discussed types of technovation access tools like digital learning platforms, Internet of Things (IoT), and Mixed Reality Tools. The study further outlined the roles of technovation access tools in enhancing information resources accessibility to include improving efficiency and accuracy, fostering collaborations through digital platforms and Natural Language Processing (NLP) for discovering insights, among many others. Enhanced search/retrieval capabilities, personalized information retrieval and strategic utilization of each of these tools through automation and knowledge management were highlighted as the advancements in information accessibility. The challenges of technovation access tools noted in the study include digital divide and accessibility inequality, privacy and security concerns, among others. Some of the ways to mitigate these challenges of was the recommendation amongst others that Government and library administrators should invest in information literacy training programmes to enhance access to information, particularly in underserved regions. The study concluded that technovation access tools have revolutionized information in the 21st century, transforming how information resources are accessed, analyzed, and utilized as well as the simplification of complex data retrieval processes, thereby driving advancements in the Library and Information Science field.
An Intelligent Machine Learning Framework for Predictive Diagnostics in Cloud-Based Database Replication Systems
Preemptive failure detection in database replication systems is an essential need for ensuring data consistency and system availability in distributed enterprise settings. This article describes a state-of-the-art machine learning framework that learns to predict replication anomalies before they appear as service-affecting failures. The suggested architecture consumes heterogeneous data streams from Oracle GoldenGate and MySQL replication scenarios, such as structured metrics and unstructured logs, and converts them into predictive features using advanced preprocessing methods. Three co-operating machine learning models—Random Forest for binary classification, Gradient Boosting for multi-class categorization, and Long Short-Term Memory networks for sequential pattern recognition—collaborate to detect elusive predictors of replication instability. The model shows marked improvement over conventional threshold-based monitoring in that it detects non-linear correlations and temporal dependencies within the telemetry data. It supports seamless integration with contemporary observability platforms, enabling real-time alerting and visualization of anomaly probability, allowing preemptive action by database administrators. The framework achieved 92.4% prediction accuracy and a 37% reduction in unplanned downtime in production-grade tests, outperforming conventional threshold-based monitoring. Comprehensive testing across production-grade environments validates the efficacy of the framework in lessening unplanned downtime without compromising on low false positive rates. This work provides a thorough methodology for using machine learning technologies for database reliability engineering that closes the gap between research and operational usage.
Bridging the Gap between Network Security and AI-Driven Threat Detection
Modern network architectures operating throughout distributed cloud environments and encrypted communication channels have rendered conventional perimeter-based safety mechanisms incapable of detecting advanced persistent threats. Artificial intelligence techniques employed by adversaries to craft polymorphic malware, automate reconnaissance activities, and cover command-and-control communications within legitimate protocol traffic require essential transformation of protective capabilities. Conventional signature-based and rule-driven detection systems are unable to evolve and identify behavioural anomalies across encrypted traffic flows, generating extensive blind spots that sophisticated attackers systematically exploit. The article discusses the strategic embedding of machine learning methodologies within network security architectures through robust data pipelines, real-time inference mechanisms, and continuous learning frameworks. Hybrid deep learning architectures, which combine convolutional neural networks with bidirectional long short-term memory components, have emerged as superior in capturing spatial features and temporal dependencies inherent in network telemetry streams. Implementation challenges include extreme class imbalance driven by rare malicious traffic samples, latency constraints necessitating millisecond-scale inference for inline enforcement, interpretability requirements enabling analyst comprehension of detection rationale, and adversarial attacks aimed at compromising training data integrity or crafting evasive inputs. Operational deployment requires comprehensive telemetry collection across heterogeneous sources, advanced feature engineering transforming raw packet data into statistical representations, and seamless integration with security orchestration systems. Augmented intelligence frameworks establishing bidirectional collaboration between automated detection systems and human analysts allow for continuous model refinement through labelled feedback loops and enable adaptive defense ecosystems capable of evolving alongside emerging threat landscapes.
The Role of IT Leadership in Ensuring Compliance in Pharmaceutical ERP Systems
Enterprise Resource Planning systems are essential technology infrastructure in pharmaceutical organizations, combining manufacturing functions, supply chain management, quality control, and regulatory reporting capabilities under increasingly tight regulatory controls. The pharmaceutical sector is confronted with distinctive compliance demands created by Good Manufacturing Practices, Good Distribution Practices, and electronic record regulations requiring thorough data integrity, traceability, and validation across product lifecycles. IT leadership is not only about traditional technology management but also includes strategic architecture for compliance, risk mitigation schemes, and governance mechanisms that help make ERP systems meet changing regulatory needs without sacrificing operational effectiveness. The present review reviews regulatory regimes that control pharmaceutical information systems, such as electronic records and electronic signatures standards, Current Good Manufacturing Practice legislation, and global GMP guidelines. IT leadership approaches towards establishing compliance-driven ERP structures are investigated via governance models, risk-based validation practices, vendor management strategies, and technology architecture choices, trading on-premises against cloud-based deployments. Key compliance features such as audit trail deployment, electronic signature workflow, user access controls, and disaster recovery functionality are discussed in detail. Emerging trends that include artificial intelligence integration, blockchain usage, cloud computing acceptance, and cybersecurity standards pose opportunities as well as challenges for pharmaceutical ERP compliance. Based on the synthesis of regulatory guidelines and industry best practices, what comes into focus is how IT leaders operate within the multifaceted environment of pharmaceutical ERP compliance and facilitate organizational goals of operational excellence, regulatory compliance, and patient safety.
AI-Augmented LCNC Frameworks for Multi-Jurisdictional Government Compliance: An Architectural Approach
Government institutions modernizing legacy infrastructure encounter persistent tension between operational standardization and jurisdictional autonomy. Traditional system replacements risk disrupting established workflows while violating locality-specific regulatory requirements that prevent uniform implementations across organizational boundaries. Low-Code and No-Code platforms address these challenges through architectural patterns separating configurable surface layers from standardized technical foundations. Workflow engines allow jurisdictions to define approval sequences matching local procedures without altering underlying system logic. Form designers enable customization of data collection requirements satisfying varied regulatory mandates. Automated service generation converts configuration specifications into deployed capabilities, compressing implementation timelines while reducing specialized technical expertise demands. Multi-tenant isolation maintains strict separation between jurisdictional data despite shared infrastructure, satisfying regulatory independence requirements. Template-driven approaches replicate proven configurations across similar localities, accelerating deployments while preserving necessary adaptations. Artificial intelligence augmentation extends capabilities beyond basic digitization. Automated payment processing routes transactions through intelligent workflows eliminating manual handling. Document classification analyzes incoming submissions and applies appropriate processing pathways. Systematic redaction identifies and obscures protected information, removing labor-intensive manual review previously required before public disclosure. Operational transformations demonstrate measurable improvements across deployment sites. Processing timelines compress from extended durations to same-day completion. Implementation cycles accelerate significantly, enabling faster service enhancements. Operating costs decrease relative to earlier expenditure levels. These architectural principles offer government technology administrators actionable frameworks for advancing modernization while satisfying regulatory obligations, with transferable applications across healthcare delivery systems, financial service organizations, and comparable regulated environments confronting parallel challenges, balancing innovation imperatives against compliance mandates.
AI in Healthcare: Ethical Considerations in Patient Data Management
The introduction of artificial intelligence into healthcare provision is both revolutionary and raises significant ethical issues that should be closely considered. The issue of patient data management seems to be one of the critical concerns when the problem of privacy vulnerability, breach of security, and unauthorized access jeopardizes the most basic rights of the people who receive medical services. Another notable issue is algorithmic bias, since the training sets that are not demographically diverse will result in systems that promote historical healthcare disparities and provide poor care to the underrepresented groups. Many artificial intelligence systems are opaque and therefore lack the transparency to support informed consent and add complexity in clinical decision-making. Black box algorithms, which do not understand how they make their decisions, cause a loss of trust between healthcare providers and patients and lead to safety concerns when unsolicited recommendations are followed. To develop sustainable artificial intelligence healthcare systems, sustainable models of governance to involve the patients as active stakeholders instead of passive sources of data are needed. Health care institutions should be accountable, show transparency in reporting, honest opt-out, and provide tangible proof of the ability to enhance the quality and outcome of care with the help of algorithmic tools. Educational programs, which clarify the artificial intelligence abilities and constraints, will enable patients to be actively involved in decision-making, resulting in their treatment and data use. It is necessary to balance technological novelty with ethics in the development of reliable systems that would benefit every population in a fair manner. The way forward requires a continuous interaction between technologists, clinicians, policymakers, ethicists, and patient advocates to set the standards that can safeguard individual rights and, at the same time, facilitate positive innovation. Technical sophistication itself will not be used to measurably gauge success, but rather how far artificial intelligence systems support the principles of human dignity, fairness, and autonomy in healthcare delivery.
Building B2B Advertising Data Intelligence Systems: AI Solutions for Enterprise Marketing
This article presents a comprehensive framework for implementing artificial intelligence in Business-to-Business (B2B) advertising and marketing intelligence systems. It examines the fundamental differences between B2B and B2C marketing approaches, highlighting why enterprise marketing requires specialized technologies to address longer sales cycles, multiple decision-makers, and relationship-focused engagement. The proposed "B2B Marketing Intelligence Framework" consists of three interconnected components: AI-powered Account-Based Marketing (ABM), Predictive Lead Intelligence, and Secure Data Collaboration. We examine practical tools like Demandbase, 6sense, and Terminus for ABM implementation; Lattice Engines, MadKudu, and Leadspace for predictive scoring; and LiveRamp, Habu, and InfoSum for secure data sharing. Through case studies of companies like Microsoft, IBM, and ServiceNow, we demonstrate how these technologies deliver measurable improvements in account targeting accuracy, conversion rates, and marketing ROI. The framework provides enterprises with actionable guidance for selecting and implementing AI solutions that enhance audience understanding, personalization capabilities, conversion prediction, and cross-organizational data collaboration while maintaining privacy compliance.
Hierarchical Semantic Caching for MCP Servers: A Multi-Tier Context-Aware Approach to Optimize AI Model Data Access
This article introduces a novel semantically-enhanced three-tier caching system designed to optimize data access for Model Context Protocol (MCP) servers that support complex AI workloads. Traditional caching approaches often treat data as generic blocks, failing to capture the intricate semantic relationships between models, data, and computational tasks. The suggested hierarchical system overcomes this drawback by merging structural caching hierarchy and semantic awareness in three special tiers: semantically-aware model segment caching, contextual metadata caching, and intelligent prefetching. Its core is a dynamic knowledge graph that captures and regularly updates complex relationships among system components. Large-scale evaluation on large language models and computer vision applications shows significant gains across various performance metrics over traditional techniques, such as dramatic data access latency reductions, improved cache hit rates, better use of resources, and reduced bandwidth usage in distributed settings. The article verifies that semantic-aware caching offers a compelling answer for solving the mounting performance needs of current-day AI infrastructure, especially for intricate models running within changing computational environments.
AI-Driven DataOps Observability: Transforming Data Reliability in Modern Platforms
Data ecosystems have evolved radically through single-centric architecture to distributed and real-time platforms across hybrid and multi-cloud environments. Conventional tracking systems have difficulties keeping track of interrelated pipelines, microservices, and data lakes that establish blind spots in operations and undermine dependability. Generative AI and Machine Learning-powered dataops observability is the paradigm shift in passive monitoring of reliability to proactive management. The AI-based observability architecture is a multi-layered framework that combines data ingestion, preprocessing, core intelligence engines, correlation analysis, and action orchestration strata. The AI-enhanced observability systems process telemetry data, trace the lineage in the dynamic dataflows, and identify anomalies before they trigger production failures. Generative models automatically encode relationships between data sets, generate transformation logic, and suggest remediation with insight into the context. To data reliability engineers, this transformation will offer an intelligence layer that constantly learns the behaviour of the system, minimizes false positives, and speeds up root-cause detection. On the incident response side, AI predicts data drift, schema incompatibilities, and spikes in throughput, transforming incident response to incident prevention through predictive analytics. The outcome of implementation shows that the incident detection and resolution metrics have greatly improved, the mean time to detect has decreased to minutes, and the system availability has increased significantly. The quality of alerts that are better has a high effect of reduction in false positives, and predictive abilities that give a preview of an incident ahead of its occurrence. The development of AI models for self-healing pipelines and autonomous governance structures can be viewed as the next step in the progression of reactive troubleshooting of a problem to a proactive reliability culture, where all phases of the data lifecycle gain the advantages of adaptive intelligence.
The Future of Software Development: Programmers and AI Pair Programming
The integration of AI programming assistants represents a paradigm shift in software development, transforming traditional workflows into collaborative human-machine partnerships. This article traces the evolution from basic code completion tools to advanced AI pair programming systems, examining their impact on productivity, code quality, and developer well-being. The transformation extends beyond technical aspects to reshape educational approaches, career development paths, and organizational structures. While delivering clear productivity benefits, AI assistants also introduce challenges related to code quality, security, and risks of developer deskilling. Ethical considerations emerge around safety-critical applications, intellectual property, and the long-term effects of automation on developer skills. The workforce landscape is evolving with new specialized roles, restructured teams, and altered global development patterns. Looking forward, advancing AI-human collaboration in programming requires overcoming these limitations. Progress will depend on explainability, multimodal interaction, domain-specialized systems, and collaborative learning models that complement—rather than replace—human expertise.
The Medallion Architecture in Data Engineering: A Layered Approach to Data Quality and Governance
The medallion architecture is a new way to handle data engineering. This method simplifies how companies deal with different types of data by refining that data step by step. It's built with three levels: bronze, silver, and gold. Each level has a specific job in the data transformation process. The bronze level takes in raw data and keeps it safe, making sure to record where the data came from and other important details. Cleaning, checking, and standardization of the data is done at the silver level. This fixes any quality issues by removing duplicates and ensuring it meets business rules. The gold level then offers data that's ready for assessment and business intelligence tools to use. The way this architecture is set up allows for data to be processed quickly and in stages. It also provides governance to help with company-wide rollouts. Compared to older data warehouse systems, this architecture is more adaptable. It also provides better organization and quality control than simple data lake methods. It's a mix of both, giving flexibility in storage and structured quality management for various company needs.
Decentralizing Supply Chains: The Socioeconomic and Ethical Revolution through Distributed Systems Integration
The supply chains of today are in a more vulnerable position than it has ever been before due to the recent global disruptions, which show the weakness of centralized operational models. The distributed systems integration is not merely efficient but a systemic solution that goes past efficiency in operations to include basic socioeconomic restructuring. Decentralized supply chains allow global market access to small businesses through blockchain technology, smart contracts, and peer-to-peer networks, at a minimum, improving transparency and resilience. Nonetheless, this change brings about some complicated ethical issues of algorithmic bias, data sovereignty, and digital colonialism, especially in marginalized groups. Regulatory frameworks are still disjointed at the jurisdictional level, and global networks have challenges in complying. Regardless of these challenges, distributed supply chains show a strong promise of establishing more inclusive, resilient, and ethically-based trade networks that redistribute economic power more fairly among players without affecting operational efficiency.
The Societal Impact of AI: Balancing Innovation with Public Trust
Artificial Intelligence technologies radically transform modern societal paradigms with revolutionary uses in healthcare, education, economic systems, and governments, while at the same time raising unparalleled moral dilemmas to be addressed with inclusive policy solutions. The health sector is enhanced by advanced diagnostic algorithms proving to be of better accuracy in oncology imaging as well as pharmaceutical development platforms, shortening drug development times using molecular modeling capacities. Educational settings are subject to increased personalization by adaptive learning platforms that adapt content presentation according to individual student understanding patterns and interaction measures. Algorithmic bias expressions, though, expose systematic differences in facial recognition technologies and criminal justice systems to perpetuate discriminatory behavior towards marginalized groups. Privacy loss arises through widespread data collection systems that allow high-resolution behavioral profiling and commercial utilization of personal data by means of targeted advertising marketplaces. Economic disruption involves a workforce shift from experienced, skilled work to technology-enabled collaborative settings, best illustrated in the construction sector shifts necessitating holistic retraining programs integrating digital fluency with domain-specific knowledge. Trust mechanisms involve responsive, explainable AI systems with the ability to make dynamic transparency adjustments based on contextual needs and user levels of expertise. Multi-stakeholder governance models exhibit better performance in balancing promotion of innovation with risk control through cooperative regulatory models embracing a variety of viewpoints from healthcare professionals, technology innovators, patients, ethicists, and policymakers to promote responsible AI deployment, safeguarding human well-being while pursuing technological advancement.
Hierarchical Perception Networks for Robust Multi-Sensor Fusion in Autonomous Vehicles
This article examines how perception systems in autonomous vehicles have developed over the years, from flat fusion pipelines to hierarchical perception networks. It talks of how these sophisticated architectures combine a variety of sensor modalities with a variety of semantic levels, enhancing resiliency under a variety of driving conditions whilst remaining interpretable and efficient. The hierarchical method allows the automobiles to make rational decisions regarding the environments as a whole, resolving all the contradictory sensory data with the contextual information to make autonomous operations safer and more trustworthy. These frameworks perform better in difficult situations where the standard methods fail by organizing fusion on many levels of abstraction, between early spatial correspondence and high-level semantic interpretation. The article examines aspects such as cross-modal alignment methodologies, contextual inferences using hierarchies of semantics, uncertainty modeling to achieve resilient functioning, and real-time implementation using optimization strategies. In addition to technical advantages, hierarchical perception networks are even more interpretable and flexible across various spheres of operations, which forms the basis of reliable autonomous systems to balance creativity and responsibility. This significant architectural evolution in perception design opens up a direction of cognitive consistent autonomy that can deal with the complexity and variety of real-life driving worlds.
The Seven Pillars of Agentic AI Implementation in Enterprise Systems
Organizational digital ecosystems face remarkable complexity as dispersed systems generate big operational information requiring smart coordination throughout safety, compliance, infrastructure, and enterprise domains. Conventional automation frameworks show insufficiency when confronting dynamic environmental shifts, emerging hazard vectors, and evolving business necessities that render static rule-based structures out of date. Agentic AI inserts autonomous skills to experience operational states via ongoing monitoring, reason approximately pleasant movements via contextual evaluation, and adopt remediation moves without human intervention. Seven foundational pillars outline end-to-end frameworks for applying self-sufficient intelligence across enterprise tactics: autonomous decision architectures incorporating perception-reasoning-action loops immediately into processes, multi-agent coordination systems orchestrating specialized domain agents through shared protocols and collective learning, continuous learning mechanisms permitting policy optimization by reinforcement feedback and experience accumulation, data governance creating transparency and fairness during decision streams, resilience capabilities looking forward to failures and implementing self-healing remediation, human-AI co-governance balancing autonomous execution with oversight needs, and scalable infrastructure allowing dispersed agent deployment. Large language models augment autonomic computing realization via natural language log interpretation and remediation synthesis. Causal inference helps to differentiate between real failure mechanisms and symptomatic correlations, facilitating successful root cause resolution. Implementation requires precise reward structure design, exploration safety boundaries, and ethical frameworks guaranteeing algorithmic fairness among stakeholder populations. Corporations implementing these architectural standards comprehend proactive working stability, insightful aid optimization, and reliable automation consistent with regulatory demands and organizational ethics.
Master Data Governance for Global Drug Launch Readiness in Healthcare Enterprises: A Systematic Framework for Compliance and Operational Excellence
Master Data Governance has emerged as a mandatory construct to pharmaceutical business venturing in the intricacies of drug commercialization in the world today, amidst the more complex regulatory demands and shorter market penetration cycles. The pharmaceutical sector has complicated issues which include compliance with regulations in different jurisdictions, implementation of serialization regulations, country-specific labeling necessity, and traceability demands of the supply chain which push complex master data administration talents. The operational necessity extends to competitive advantage: improved launch preparedness, regulatory compliance guarantee, and supply chain resilience, which is the business case of strong Master Data Governance. The incremental value approach of Phased implementation methodologies enables pharmaceutical organizations to build the Master Data Governance maturity incrementally as they address the immediate business requirements and prove the incremental value over the deployment lifecycle. Hierarchical master data architecture assists in global product templates with market extensions that are market-specific and enable the effective management of labeling requirements in multiple regulatory jurisdictions, and maintain consistency in core product information. Serialization and master data systems enable pharmaceutical manufacturers to address the global track-and-trace requirements, as well as obtain strategic gains that can encompass counterfeit prevention, enhancement of visibility across the supply chain, and the possession of speedy recall functionalities. Blockchain-based distributed ledgers, analytics-driven artificial intelligence, and workflow coordination technologies are all available on the technology platforms and have the potential to transform Master Data Governance into a strategic facilitator of operational effectiveness and competitive advantage in global pharmaceutical markets.
Identity and Access Governance in Network Security Architecture
Modern virtual ecosystems face unparalleled protection-demanding situations as groups more and more shift closer to the cloud and distributed teams of workers. Conventional perimeter-based protection fashions have fallen short in protecting sensitive information and sources in dynamic computing environments. The article looks at a critical merge between identity governance mechanisms and network security architecture, which addresses the fundamental disconnect between legacy access control frameworks and threat landscapes that are increasingly modern, sophisticated in their attack vectors, and persistent in their adversaries. Evolution from function-primarily based get right of entry to models to characteristic-primarily based and zero-trust architectures is a paradigm shift that emphasizes continuous verification without implicit accept as true with assumptions. Integrating gadget studying techniques permits for behavioral analytics and detection of anomalies that pave the way for proactive threat mitigation earlier than safety incidents strike. Strategies for implementation within the healthcare, financial services, and retail sectors provide a working view of how automated governance platforms, in combination with next-generation network security architecture, work together in strengthening the organization's security posture. The blockchain-based totally frameworks tune the immutable audit trails of healthcare data access, at the same time as recurrent neural networks examine the temporal patterns in financial transactions to detect fraudulent activities. Certainly, corporations that effectively integrate identity governance with network protection architecture are better positioned to deal with present-day and rising cyber threats-even as driving operational performance, ensuring regulatory compliance, and maintaining stakeholder consideration of their virtual transformation initiatives.
Multi-Cloud Optimization: Orchestrating Workloads Across Heterogeneous Cloud Environments
Multi-cloud optimization is a revolutionary model in enterprise cloud computing that overcomes the strategic drawbacks embedded in single-provider reliance through intentional workload allocation to diverse cloud service providers. The architecture consciously accepts heterogeneity as a strategic advantage, allowing organizations to break beyond individual provider bounds while taking advantage of unique capabilities, pricing models, and geographic footprints. Resource abstraction by containerization technologies and infrastructure-as-code models creates single interfaces for provisioning and management of various cloud resources, and dynamic orchestration mechanisms allow real-time traffic steering and workload transfer across cloud boundaries. Technical issues like networking infrastructure complexity, identity and access management heterogeneity, data sovereignty needs, and security posture homogenization require advanced interoperability solutions covering software-defined networking, federated identity standards, and cloud security posture management solutions. Cost optimization goes beyond mere price comparison to include performance-normalized pricing analysis, dynamic resource allocation using spot instances, reserved capacity portfolio optimization, and algorithmic methods using machine learning for predictive resource planning. Application areas ranging from disaster recovery, distributed artificial intelligence operations, compliance-based architectures, and worldwide e-commerce platforms illustrate the applied value of multi-cloud strategies in resolving operational resilience, regulatory compliance, and performance optimization needs across geographically dispersed infrastructures.
Investigating the Effectiveness of Supervised and Deep Learning Models: An Assessment for Binary Intrusion Detection in Structured Network Traffic
Introduction: The growing complexity and frequency of cyber threats necessitate more adaptive and accurate intrusion detection systems (IDS). Traditional rule-based systems often fail to detect emerging cyberattacks patterns, underscoring the importance of data-driven approaches. Machine-learning methods, particularly deep learning and supervised learning, often promising capabilities for detecting anomalies in complex network environments. This study addresses the rising need for robust IDS models capable of distinguishing between benign and malicious traffic with high precision. Objectives: The primary objective of this research is to perform a comparative assessment of four supervised learning models, including Multi-Layer Perceptron, Bagging Tree Ensemble, CatBoost, and One-Dimensional Convolutional Neural Network, for binary intrusion detection. The study aims to examine their performance in terms of accuracy, precision, recall, and F1-score, thereby identifying the most effective classifier for structured network traffic data. Methods: A labeled network traffic dataset encompassing 4,000 records and 25 attributes was used. The dataset included both categorical and continuous variables. There was a class imbalance, with most of the instances being benign network traffic. Preprocessing was done by applying one-hot encoding to categorical features and z-score normalization to numerical features. All models were trained based on an 80:20 stratified train-test split and evaluated by traditional performance measures. Results: CatBoost exhibited the highest overall performance among the models, attaining an accuracy of 0.89, a precision of 0.99, and an F1-score of 0.86. The Bagging Tree model attained perfect precision (1.00). The 1D CNN model demonstrated competitive recall and F1-score. The MLP had poor recall due to the increased number of false negatives. The results of the study have shown the efficiency of ensemble and deep learning approaches for binary intrusion classification. while also revealing the inherent precision-recall trade-offs among different models. Conclusions: The study underscores the effectiveness of CatBoost and other ensemble-based approaches for intrusion-detection in structured network traffic datasets. It also emphasizes the importance of selecting models based on organizational risk tolerance. The research provides a practical performance benchmark to guide future IDS model selection and optimization in cybersecurity applications.
Attention-Enhanced Deep Feature Fusion: A Comprehensive Framework for Multimodal Sentiment Analysis
Multimodal Sentiment Analysis (MSA) is an evolving area absorbed on capturing multifaceted human feelings by integrating diverse data types, inclusive of text, audio, and visuals. Leveraging multiple modalities enhances sentiment prediction by enriching the representational capacity of model architectures. Addressing the challenges in this domain, we put forward a novel approach that unites advanced feature extraction techniques—BERT for textual data, Wave2Vec2 for acoustic data, and Vision Transformer for visuals—with BiLSTM networks augmented by self-attention and multi-head attention mechanisms. The proposed architecture effectively extracts and fuses modality-specific features to construct a robust multimodal representation. Evaluated on the benchmark CMU-MOSI dataset, the projected model achieves 82.45% accuracy with the self-attention mechanism and 86.04% with the multi-head attention mechanism, surpassing several transformer-based states of the art approaches. This superiority stems from the handcrafted feature extraction, effective fusion strategy, and the ability of BiLSTM with multi-head attention to seize diverse relationships without overfitting. The findings highlight that hybrid architectures that integrate the advantages of transformers with attention-augmented recurrent models can outperform pure transformer-based designs for multimodal sentiment analysis.
Bridging Pedagogy and Computation: A Theoretical Framework for AI in Educational Systems
Introduction Today the integration of Artificial Intelligence (AI) in education systems is a paradigm shift in in building, delivering, and assessing knowledge. Though many studies address AI applications and implications, their theoretical underpinnings are scattered across disciplines. AI-powered adaptive learning software, intelligent tutoring systems, automated grading, and predictive analytics are being used in educational institutions increasingly, generally without an explanatory theoretical understanding of how these technologies alter learning processes. Objectives: This paper synthesizes key theoretical perspectives of constructivism, cognitive load theory, personalized learning, and socio-technical systems theory, to build an integrative framework explaining AI’s pedagogical role. It also addresses how AI-backed systems operate and confront learning theories, focusing on the interplay among technological affordances, learner agency, and institutional dynamics. Methods: An extensive review of the literature was conducted across learning sciences, cognitive psychology, information systems, and critical theory, including articles published between 2013 and 2025. Adaptive learning systems, intelligent tutoring systems, and AI-based personalisation models are discussed to reveal cognitive processes and implementation implications at individual, social, and institutional levels. Results: Five theoretical frameworks were recognized as fundamental: constructivism for AI-enhanced learning environments, cognitive load theory for the design of intelligent systems, connectivism and networked intelligence, adaptive personalization theory, and socio-technical systems theory. Main results suggest that AI can be a cognitive tool, instructional mediator, social agent, and institutional technology, yet a multi-level theoretical appreciation of its educational use is required. Conclusions: AI learning needs to be envisioned through a composite theory model that combines cognitive, social, and institutional aspects. The model suggested here focuses on personalized scaffolding, transparent and explainable algorithms, equitable and bias-aware design, and alignment between technological efficacy and genuine learning objectives. The study finally underscores the imperatives of theory-driven innovation where pedagogical theory would inform AI innovation for improving learning without undermining human agency and equity.
Interpretable and Robust Machine Learning for Alzheimer’s Disease Diagnosis: A Hybrid SHAP-Boruta-STARS Framework
Introduction: Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder with high societal and clinical impact. Early detection remains challenging due to complexity of biomedical data and the presence of imbalanced datasets.Machine learning offers promising solutions, but interpretability and robust feature selection are critical for reliable predictions. This study aims to develop a robust and interpretable machine learning framework for AD prediction that integrates a hybrid feature selection methodology combining:SHapley Additive exPlanations (SHAP) for interpretability, Boruta for statistically relevant feature identification, and Stability Selection and Ranking (STARS) for robust feature stability. We developed a novel hybrid feature selection framework for AD prediction combining data preprocessing, hybrid feature selection, and multi-model evaluation. In this framework, after a data preprocessing, a hybrid feature selection approach integrated Boruta, SHAP, and STARS methods was developed to identify the most stable and relevant features. Selected features were used to train various classifiers, including Logistic Regression, SVM, Random Forest and XGBoost,evaluated using 5-fold stratified cross-validation with SMOTE oversampling applied to mitigate class imbalance. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC, with optimal decision thresholds tuned for each model. Two complementary statistical tests were employed (paired t-test and Wilcoxon) to evaluate significant differences between models.The hybrid feature selection framework significantly improved model performance for AD prediction. Among the tested models, ensemble methods outperformed traditional classifiers; particularly the Random Forest model demonstrating superior accuracy, precision, and recall, statistical analysis confirmed its significant advantage over other models. These results demonstrate the effectiveness of the proposed hybrid feature selection and ensemble learning approach for accurate and robust AD prediction. The proposed hybrid SHAP-Boruta-STARS framework provides a comprehensive, robust, interpretable, and statistically validated approach for Alzheimer’s disease prediction. It effectively identifies key features and supports reliable model selection, offering a promising tool for clinical decision support and early diagnosis.
Inverse Neuro-Fuzzy Control of Nonlinear Systems
This article, we addressed the use of Artificial Intelligence techniques in the field of control engineering. Our work focuses on the use of neuro-fuzzy networks, specifically ANFIS (Adaptive Neuro-Fuzzy Inference System), for the identification of inverse models required for implementing the control laws of a nonlinear dynamic system. In the first approach, the identified model is used as an open-loop controller with the system (Direct Inverse Control) for regulation purposes. In the second approach, Internal Model Control (IMC) is applied to improve the performance of the neuro-fuzzy model when the system is subject to a constant disturbance. The final section presents an application of these control structures to a nonlinear system. The results are validated through simulations carried out in the MATLAB environment.
Artificial Intelligence as a Catalyst for Institutional Transformation in Higher Education
Introduction: Artificial intelligence (AI) has evolved from a technological innovation into a strategic catalyst for institutional transformation in higher education. This study explores how AI supports the modernization of academic structures, pedagogical practices, and decision-making processes, moving beyond traditional models of technology adoption toward systemic educational innovation. Objectives: This work aims to examine how AI contributes to the modernization of higher education by identifying key factors influencing its adoption. It also seeks to develop a strategic framework that aligns technological integration with institutional objectives, leadership practices, and organizational culture to foster sustainable academic transformation. Methods: A mixed-methods approach was applied, combining quantitative and qualitative techniques. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) and Sociotechnical Systems Theory, the study used Likert-scale surveys and focus group discussions with faculty and administrators to analyze individual and organizational perspectives on AI implementation. Results: Findings reveal that successful AI integration depends not only on user training and technological proficiency but also on leadership engagement, institutional culture, and alignment with educational goals. Interdisciplinary collaboration and ethical awareness emerged as essential components for achieving meaningful and sustainable innovation through AI in higher education. Conclusions: AI adoption in higher education represents a shift from isolated technological use to systemic innovation. A strategic framework is proposed emphasizing continuous professional development, ethical governance, and collaborative engagement to ensure that AI enhances quality, inclusiveness, and sustainability across institutional and pedagogical dimensions.
Soft Sensor for Determining Municipal Wastewater BOD and COD from Excitation-Emission Matrix Coupled with Partial Least Square Regression
Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) are critical indicators for assessing the organic pollution load in municipal wastewater. However, conventional BOD (BOD₅) and COD analytical methods are slow, hazardous, or require extensive laboratory preparation, limiting their use for real-time monitoring and rapid decision-making in wastewater treatment plants. This study proposes a soft-sensor approach based on fluorescence Excitation–Emission Matrix (EEM) spectroscopy coupled with multivariate analysis to rapidly estimate BOD and COD in municipal wastewater. Wastewater samples were collected from a treatment plant and pre-processed through filtration, pH adjustment, dilution, and scatter removal. The resulting three-dimensional EEM datasets were analyzed using Parallel Factor Analysis (PARAFAC) to identify dissolved organic matter (DOM) fluorophore components, and Partial Least Squares Regression (PLSR) was applied to develop predictive models for BOD and COD estimation. PARAFAC identified two dominant fluorophore groups: a tryptophan-like, protein-like component (Ex/Em ≈ 275/340 nm) and a humic-like component (Ex/Em ≈ 350/437 nm), consistent with known wastewater DOM profiles. While PARAFAC successfully characterized DOM, it showed limited predictive capability for BOD estimation when used alone. In contrast, PLSR demonstrated strong predictive performance, effectively reducing data dimensionality and correlating EEM spectral variations with measured BOD and COD values. The integration of EEM fluorescence spectroscopy with PLS regression shows high potential for developing rapid, reagent-free, and real-time soft sensors for wastewater monitoring, enabling timely operational decisions in wastewater treatment plants.
Development and Implementation of Online Student Evaluation for Teachers (OSET): An Assessment Tool in Measuring the Faculty Teaching Performance
Student evaluation for teachers is a way to assess the educators' teaching meth-ods, communication skills, subject mastery, and classroom management. It is deemed necessary to address areas needing improvement and to enhance the teaching quality of faculty members. The traditional paper-based evaluation pro-cess is time-consuming and cumbersome. It is also prone to human errors, which may compromise the accuracy and credibility of the results. Therefore, this study focuses on the development and implementation of an online student evaluation for teachers at a State University in the Philippines. Its aim is to streamline the collection, storage, and processing of students' feedback and to generate students' evaluation reports. The Rapid Application Development model was used to de-velop each module of the web system. The graphics were designed using Adobe Photoshop and Canva. The web system's functions and modules were developed using PHP, JavaScript, and CSS, while the database was created using MySQL. The developed system was integrated into the University's web student portal. Each function of the web system was thoroughly tested, and the initial results of alpha testing revealed promising results, suggesting continued implementation of the technology. The study proved that OSET is functional and capable of generat-ing real-time reports, which are crucial in improving teaching quality and foster-ing educational excellence.
Cloud-Native Hcm Architectures: Performance, Scalability, and Security
In the era of digital acceleration, enterprises are rethinking their Human Capital Management (HCM) strategies to align with the expectations of a dynamic, global workforce. As legacy systems struggle to keep pace with modern operational demands, cloud-native architectures have emerged as a foundational enabler of next-generation HCM platforms. These architectures offer the flexibility to deploy services independently, scale resources automatically, and maintain system integrity under varying workloads—all while fostering rapid innovation. This paper explores how core cloud-native elements such as microservices, container orchestration, and API-first ecosystems are transforming the design and delivery of HCM solutions. It investigates techniques that enhance performance—including autoscaling, distributed caching, and intelligent load routing—while highlighting methods to sustain availability and responsiveness in high-demand environments. Moreover, it addresses critical security concerns specific to multi-tenant cloud deployments, including identity governance, data protection, and adherence to regulatory frameworks such as GDPR and HIPAA. Drawing from industry practices and real-world case examples, the study presents a detailed perspective on how cloud-native strategies are redefining performance benchmarks, enabling elastic scalability, and strengthening security for modern HCM infrastructures.
Development of Creative Abilities of Pupils Through Solution Tasks on Physics Using Computer
Introduction: It is known that nowadays various specialists widely use computer in their work. Today, one can calculate, write, read, edit, study, draw, rework, create audio and video and play on a computer. Its capabilities are increasing day by day, so it has become a person’s most reliable friend at work, study, at home, and at leisure. Objectives: Information technologies in physics teaching can be used in various forms - in preparing presentations, using ready-made educational and demonstration programs, conducting virtual laboratories, solving tasks, etc. The teacher must understand that use of information communication technologies is not only a modern requirement, but also an indispensable condition for achieving high-quality education. His task is to organize teaching that will help increase the effectiveness of education and develop students' creative abilities. For this it is necessary to help students in reflecting, thinking and drawing their own conclusions using computer. Use of computer allows us to create an opportunity to develop students' creative activity and help to teach them solving non-standard tasks by developing independent and logical thinking. Methods: Processing information sent from spacecraft, controlling the motion of particles in accelerators, and solving complex problems of theoretical physics are unimaginable without computers. Almost all physical problems encountered in life cannot be solved analytically and require the use of computers to solve them. Therefore, every physicist must be able to independently solve problems on a computer, and conducting scientific research in this area is one of the most pressing problems of our time. Results: In order to simplify the solution of physics tasks on a computer the authors created the “Universal Calculator” program. The program is written in Visual Basic using numerical methods and has the ability to plot an arbitrary function graph, solve quadratic, transcendental and differential equations, calculate definite integrals and process experimental results using the “Least Squares” method. The article provides examples of calculating the path traveled by a body using the program and processing experimental results using the “Least Squares” method. Conclusions: It is undeniable that information communication technologies are a reality of modern teaching. When computers are used in physics lessons, students participate more actively in the lesson, the attitude to work changes even among the most problematic students, and the teacher is required to master the capabilities of information communication technologies, carefully think over the content of the lesson and plan the work of students at each stage of the lesson.
Problems and Proposed Solutions to Improve Freight Transportation, Case Study
Transportation is not just about getting from point A to point B, it's about coordination, accuracy and trust. The transport of goods is an integral part of the city's economy and is a prerequisite for sustainable urban life, but it causes a number of negative effects on the environment followed by air pollution and increased noise. The problems arising from road freight transport are constantly overlooked. The subject of analysis in this paper is the city of Bitola. We are witnessing the daily intertwining of different streams of goods, materials and cargo through the city's street network. To meet the needs of the inhabitants, the following supplies of goods are present: trade goods, finished products, semi-finished products, raw materials, building materials, consignments, secondary raw materials, waste and the like. With the help of a month-long survey of truck drivers in the territory of the city of Bitola, we learned about the problems they face. Based on this as a conclusion, a proposal is given for measures to improve the delivery of goods and the general flow of traffic.
The Influence of Good Corporate Governance, Human Resources on Company Performance Through Corporate Culture: A Case Study of an Iso-Certified Company
Background: Company performance is strongly influenced by sound governance, the quality of human resources (HR), and a conducive corporate culture. As a seaweed processing company, the company under this study faces challenges in meeting production targets and operational efficiency. Efforts to improve performance need to be examined from the perspective of the interrelationship between Good Corporate Governance (GCG), HR, and corporate culture. Objective: This research seeks to examine how good corporate governance affects a company’s performance, with corporate culture serving as the mediating factor. Methods: This study utilized a quantitative research design with data gathered from 180 respondents through a structured questionnaire. The analysis was carried out using the Partial Least Squares (PLS) method, processed with SmartPLS 3.0 software. The model was tested for validity, reliability, and hypothesis testing for both direct and indirect effects among variables. Results: The R² value for the corporate culture variable was 0.639, meaning that Good Corporate Governance (GCG) and Human Resources (HR) together accounted for 63.9% of the variance observed in corporate culture. The R² value for company performance was 0.582, meaning 58.2% of performance was explained by GCG, HR, and corporate culture. The direct influence of HR on corporate culture was 0.589 and on performance was 0.428 (significant). GCG had a direct effect on corporate culture at 0.297 (significant), but only 0.108 on performance (not significant). Corporate culture had a direct significant effect on performance at 0.344. The indirect influence of GCG on performance through culture was 0.102 (significant), and that of HR was 0.203 (significant). HR was the most dominant variable in the model. Conclusion: Company performance is significantly influenced by HR and corporate culture, while the influence of GCG is indirect through corporate culture. These findings imply that strengthening HR and cultivating a strong organizational culture are key strategies must implement by the company to enhance sustainable organizational performance.
The Impact of Foreign Direct Investment, Innovation, and Institutions on Economic Growth in Vietnam
This study was conducted to identify and evaluate the impact of foreign direct investment, innovation and institutions on Vietnam's economic growth. This study uses the Error Correction Vector Model (VECM) estimation method and Johansen integrated co-analysis to analyze the short- and long-term impacts of foreign direct investment, innovation and institutions on Vietnam's economic growth in the period 2002 – 2022. The quantitative findings indicate that, in both the short term and the long term, FDI exerts a negative impact on economic growth, while innovation and institutional quality positively contribute to economic growth in Vietnam
A Systematic Study of E-Governance Infrastructure for Smart Campus Development in CBSUA
The integration of Information Technology and Management in smart campus settings is a big problem that needs to be addressed. The integration of Information Technology and Management in smart campus settings is a big problem that needs to be solved today. The institution to move forward, it is important to make sure that these areas are working together well. Conducted the systematic review in 2025 to trace the development, pinpoint the challenges, and map the possibilities still open to the e-governance backbone at Central Bicol State University of Agriculture (CBSUA), to sketch the path to the “University 4.0” goal. Using the PRISMA framework, gathering the findings from 14 journal articles to official reports published from 2020 to 2025. The evidence collected through the research indicates that the basic foundational CBSUA services have been established and integrated. There seems to be a lack of seamless integration and alignment between individual software systems, significant discrepancies in the digital infrastructure across the four campuses, and deficiencies in the faculty and staff in managing data. Artificial Intelligence, Big Data, and the Internet of Things (IoT) are used in the strategic planning of fostering personalized education, proactive advising, and responsive governance. The study contends that moving forward entails an executive strategy that integrates the basic services and infrastructural disparity and advances the development of digital and analytic competencies in a stepwise manner, rather than pouring resources into emergent technologies.
Enhancing Lightweight SAR Image Classification Using MobileNetV3-Small Implemented with Convolutional Block Attention Module (CBAM)
The rapid worldwide advancement of environmental remote sensing technologies has created a growing need for automated, efficient, and high-performing analysis methods for Synthetic Aperture Radar (SAR) imagery. SAR is particularly valuable because it provides consistent imaging capabilities regardless of weather or lighting conditions, making it ideal for crucial applications such as environmental monitoring and disaster management. Despite these advantages, SAR image classification remains a challenging task due to the complex nature of SAR data, which often includes speckle noise, limited textural information, and high computational demands. To address these challenges, this study introduces an efficient and lightweight Convolutional Neural Network (CNN) architecture based on MobileNetV3-Small, integrated with a Convolutional Block Attention Module (CBAM). The attention mechanism enhances the extraction of meaningful features while reducing unwanted backscatter effects in SAR feature maps. The proposed hybrid model was trained and evaluated on a Sentinel-1 SAR dataset comprising four land cover categories: Agriculture, Barren Land, Grassland, and Urban areas. Implementing data augmentation and transfer learning techniques, the model was trained over 50 epochs, and achieved the high accuracy of 95.74%. This results, outperforming the original MobileNetV3-Small backbone, stems directly from the implementation of CBAM attention mechanism, allowing the proposed model to successfully emphasize relevant spatial and channel features. In this work, it was demonstrated that the proposed MobileNetV3-Small with CBAM architecture delivers a powerful balance between computational efficiency, robust accuracy, and generalization, capability, making it a suitable for integration in near-real-time applications and embedded systems. The results prove the potential of lightweight attention- augmented CNN architecture for a generation of autonomous and intelligent Earth observation systems, offering opportunities for further research in global ecological monitoring.
Multi Head Attention Transformer for Arabic Scene Images Text Recognition
The worldwide video library continues to expand rapidly, which creates an increasing need for modern and reliable techniques for video processing and text indexing. In this paper, we introduce a new implementation of the Transformer architecture for scene text recognition. This work comes from a comparative study between two approaches: using convolutional feature maps as input to the Transformer encoder, and fully removing any CNN component. During training, we used almost all available public datasets; however, they were still not enough because of the significant lack of large-scale and diverse datasets for this task. This challenge led us to create and publish a new artificial dataset called IYaD. The IYaD dataset currently contains around 1,400,000 images for one font and the same scale for 16 additional fonts. Each image is provided in three different versions and includes Arabic labels, Latin transcription, and the text content. The experimental results show that our Transformer-based ASTR model surpasses state-of-the-art methods, especially when trained on the IYaD dataset, establishing new benchmarks in accuracy and robustness. We believe that this dataset demonstrates the importance and potential of artificially created datasets, and it may encourage similar dataset generation in other research domains.
Using Genai for Synthetic Data Generation in Cybersecurity and Compliance
The increasing nature of the cybersecurity threats and high-pressure requirement of the norms compliance have resulted in the need to think differently about how data is managed and secured. Generative Artificial Intelligence (GenAI) provides a potential solution, incorporating the formulation of artificial data and developing these data with a high level of safety, privacy, and confidentiality, which implies guaranteed testing, training, and compliance auditing without any security concerns. GenAI models produce synthetic data that mimics the statistics of unprecedented data, allowing the security field to use generated data without revealing sensitive data to vulnerable access; this makes it particularly appealing in techniques as threat modelling, anomaly detection, simulators of red-teaming, and similar ones. Moreover, artificial datasets can be utilized in ensuring regulatory adherence because they enable testing of systems across various environments based on the type of rules that should govern data use. In spite of this potential, synthetic data generation in GenAI has various challenges, such as data fidelity, bias reprocessing, and regulatory objects. Nevertheless, the progress of GenAI models like GAN, transformers, and diffusion models takes place and enhances the reliability and security of generated data. This paper addresses how GenAI helps in cybersecurity and compliance, evaluates the advantages and disadvantages of the technology, and reflects on recent research and practical opportunities flag identify possible ways to integrate the new technology through the lens of current studies and real-life examples.
The Impact of Investment Opportunity Set and Profitability on Accounting Conservatism in ASEAN Countries
Financial statements deliver critical insights into a company's financial position, adhering to Generally Accepted Accounting Principles (GAAP) and emphasizing conservatism. This principle prioritizes prudence by recognizing costs promptly while delaying revenue acknowledgment, which can potentially distort a company’s true financial health. This research aims to analyze the influence of financial attributes on accounting conservatism, focusing on the investment opportunity set (IOS), leverage, profitability, and firm size in ASEAN's industrial sector. The research comprises a total of 2,968 firm-years, consisting of 424 companies from six ASEAN countries during the period from 2017 to 2023. The data used is sourced from the Refinitiv database obtained from the company's annual reports and employs panel data regression analysis. The research shows that the major independent variable, IOS has no effect on accounting conservatism. Conversely, the independent variable profitability has a positive impact on accounting conservatism. Additionally, two other variables, namely leverage and firm size, have statistically significant impacts on accounting conservatism. This study indicates that an increase in company size correlates with a heightened application of accounting conservatism, whereas greater profitability tends to diminish the application of accounting conservatism within manufacturing firms. The study finds that investment opportunity set does not significantly affect accounting conservatism; profitability, leverage, and firm size influence it differently. It recommends a broader focus and additional variables for comprehensive results.