HighTech and Innovation Journal
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317 research outputs found
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Mathematical Approaches and Algorithms in Big Data Architecture and Hybrid System Efficiency
Full text linkThis article presents a formal demonstration of a hybrid big data processing architecture that combines the fault tolerance and storage robustness of Hadoop with the speed and in-memory processing capabilities of Apache Spark. The proposed architecture is evaluated through test execution and performance benchmarking in real-world data centers across three regions in Kazakhstan. The model integrates distributed resource management components, Directed Acyclic Graph (DAG)-based scheduling mechanism, and Resilient Distributed Datasets (RDDs) to enable dynamic workload distribution and rapid failure recovery. The results demonstrate that the hybrid system consistently outperforms standalone Spark and Hadoop architectures under variable workloads, illustrating enhancements in execution time, task recovery, and resource utilization. Quantitative performance metrics allow for a structured comparison of architectures and help optimize deployments for diverse scenarios. The proposed hybrid architecture shows significant improvements, reducing average execution time by up to 38% and increasing resource efficiency by 25% compared to standalone Spark and Hadoop systems
Breast Cancer Classification Using Deep Feature Extraction and Machine Learning
Full text linkEarly and accurate breast cancer diagnosis remains critical yet challenging in routine practice. This study proposes a simple, reproducible pipeline that combines deep feature extraction from pre-trained CNNs (ResNet50, VGG16, EfficientNet-B0, DenseNet121, MobileNetV2) with classical machine-learning classifiers (logistic regression, SVM, k-NN, decision tree, random forest, gradient boosting, XGBoost, LightGBM, Naïve Bayes, and MLP). Features are computed after standardized preprocessing; class imbalance is addressed with SMOTE when present. We evaluate three image datasets (binary and multiclass) using accuracy, precision, recall/sensitivity, F1, and confusion matrices, and apply paired statistical tests across cross-validation splits. Findings: EfficientNet-B0+MLP and ResNet50+MLP achieve peak accuracies up to 99.6% on high-quality, balanced data, while DenseNet121+MLP with SMOTE attains 97.8% on imbalanced multiclass data. SMOTE yields substantial gains on imbalanced data and negligible effect on balanced sets; decision trees underperform consistently. Novelty/Improvement: Rather than a monolithic end-to-end network, we provide a modular, resource-aware blueprint that (i) disentangles feature extraction from classification, (ii) quantifies when imbalance correction matters, and (iii) reports clinically relevant error types. We further outline explainability with Grad-CAM/SHAP and discuss inference-time trade-offs and real-world workflow integration, offering an interpretable and deployment-friendly alternative to heavier end-to-end models
An Object Driven Decision Model for Quantifying the Virtual Merkus Pine Tree's Environment Contribution
Full text linkA tree planted in the wild contributes significantly to nature and its surroundings. Key benefits include biomass production and the strengthening of soil contours. Biomass itself is a tangible output of living organisms, offering both renewable fuel potential and notable economic value. Additionally, the presence of a tree has a considerable effect on soil shear strength, which plays a crucial role in supporting reforestation efforts in deforested areas. The research aims to construct a computational decision model of a virtual Merkus Pine tree to estimate biomass production and evaluate its impact on soil reinforcement as part of the tree's environmental contributions. The model was constructed via two types of methods: an object-oriented approach for technical design and functional-structural plant modeling (FSPM) as a core method to construct a 3D virtual pine tree model. The model is a novel computational decision model operated to visually simulate the growth and development of Merkus Pine, estimate biomass yield, and calculate annual soil shear strength due to the tree’s presence. Simulation results indicate that a single Merkus Pine tree can produce up to 242.27 kg of biomass and enhance soil shear strength by approximately 0.88 N by the end of 15 years
A Systematic Literature Review on Resilient Digital Transformation, Examining How Organizations Sustain Digital Capabilities
Full text linkIn an era marked by relentless technological shifts and market volatility, digital transformation (DT) alone is insufficient. Organizations must develop Resilient Digital Transformation (RDT)—the organizational capabilities required to sustain DT over a medium-term horizon—to navigate these challenges effectively. This study primarily aims to propose a guideline for fostering RDT. Drawing on the PRISMA guidelines and a systematic review of 77 peer-reviewed papers, this study identifies and synthesizes key targets and drivers across three core pillars: Technology, Organization, and External Environment. These elements collectively foster organizational resilience. Specifically, this study highlights how adaptability, innovation, and scalability form the technological underpinnings of sustained digital maturity; meanwhile, effective governance frameworks, ongoing workforce development, and supportive cultures promote organizational agility. Externally, proactive stakeholder engagement, responsiveness to market shifts, and robust regulatory compliance help ensure the long-term viability of digital initiatives. The findings contribute to the existing literature by unifying an integrative framework illustrating how organizations can sense, seize, and reconfigure resources to embed resilience across strategic and operational processes. By moving beyond static maturity models, the framework stresses the continuous nature of digital transformation, offering both academics and practitioners a structured approach to sustaining competitive advantage amid incessant disruptions
Novel Management Model for Leveraging Leadership for Successful Digital Transformation in Telecommunications Enterprises
Full text linkDigital transformation (DT) is crucial for improving telecommunications efficiency and competitiveness. This study examines the role of change leadership in driving successful DT in Vietnamese telecommunication enterprises, focusing on its impact on employee engagement, employee commitment, DT communication, and DT capacity. A mixed-methods approach was employed, combining qualitative insights with quantitative data from surveys of management personnel overseeing digital transformation projects. Data were analyzed to assess direct and indirect relationships using structural equation modeling. The results indicate that change leadership is a significant driver of DT success with the strongest direct effect on employee commitment. Additionally, employee commitment and digital transformation communication positively influence success through their indirect effects on an enterprise’s DT capacity. Leadership plays a critical role in fostering commitment and aligning effort with DT goals. This study introduces a novel paradigm illustrating the interplay of various interrelated factors influencing the effectiveness of digital transformation, distinguishing it from previous studies that examined these factors in isolation. This approach provides novel insights, especially regarding Vietnamese telecommunications, a domain inadequately examined in previous studies on leadership-driven digital transformation initiatives
Examining User Satisfaction and Continuous Usage Intention of Digital Financial Advisory Platforms
Full text linkThis study investigates the factors influencing user satisfaction (US) and continuous intention (CI) to use digital financial advisory platforms in Indonesia, focusing on perceived ease of use (PEU), perceived enjoyment (PE), and service quality (SQ). Data from 413 respondents were collected through an online survey and analyzed using structural equation modeling (SEM) with SmartPLS. Results revealed that PEU significantly influences PE (β = 0.923, t-value = 88.677, p < 0.001) and CI (β = 0.471, t-value = 13.950, p < 0.001), demonstrating the critical role of usability in enhancing user engagement. PE positively affects US (β = 0.211, t-value = 7.248, p < 0.001), while SQ strongly predicts US (β = 0.773, t-value = 29.423, p < 0.001). The strong impact of the US on CI (β = 0.518, t-value = 15.117, p < 0.001) highlights satisfaction's importance for user retention. R-squared values of 0.851 for PE, 0.876 for US, and 0.878 for CI indicate substantial explanatory power. This study extends the Technology Acceptance Model (TAM) by integrating enjoyment and SQ, offering a comprehensive framework for understanding user behavior in digital finance. Findings underscore the need for user-friendly design, engaging features, and high service standards to enhance satisfaction and retention. Doi: 10.28991/HIJ-2025-06-01-015 Full Text: PD
Eco-Friendly Materials for Temporary Use in Architecture and Decorations
Full text linkThis paper introduces the development of ecologically friendly composite materials for decoration and architectural purposes. The composites designed comprised degradable polylactic acid (PLA) and sugarcane bagasse fiber (SC) derived from the bioplastics and sugar industries. The SC reinforcement was examined for impurity treatment and composite formation using hot compression molding at 200 ± 10°C. Two processing methods were studied: (1) random dispersion of SC at 0, 2, 4, 6, 8, and 10 wt%, and (2) single and double-layer SC composite sheets made with 6 wt% SC. The physical and mechanical properties of the PLA-SC composites were evaluated through the morphologies and flexural properties (ASTM C293), thermal conductivity (ASTM C518), and biodegradation assessment (ISO 16929:2021). Results revealed that impurities in SC were effectively removed using an alkaline sodium bicarbonate solution followed by boiling in a 5% vinegar solution. Increasing SC contents reduced the weight, density, and thermal conductivity (k-value) of the PLA-SC composites compared to those representing single and double layers of SC. Additionally, this approach enhanced the flexural properties of the composites. Random dispersion with 10 wt% treated SC yielded the best results among the tested methods, making it the optimal approach for sustainable decoration and architectural materials. Doi: 10.28991/HIJ-2025-06-01-06 Full Text: PD
Assessment of Fresh Water Reallocation by Treated Wastewater for Irrigation
Full text linkThis study investigates the economic feasibility and farmer acceptance of utilizing treated wastewater (TWW) for agricultural irrigation in the Northern Jordan Valley (NJV). Despite its potential to mitigate water scarcity, concerns about soil health, crop yield, and land utilization hinder widespread adoption. The research measures farm profitability and farmers' willingness to embrace TWW through various blending scenarios with traditional surface water sources, incorporating a yield response function to salinity within the profit function. Results reveal that TWW adversely affects salt-sensitive crops like citrus, with net profit declining from US 5,152/ha at 100% TWW. Conversely, crops such as date palms and olives maintain stable profitability, with date palms showing minimal variation around US 714/ha at 100% TWW. The net value added for citrus decreases from US 0.46/m³, while date palms increase from US 1.41/m³, indicating resilience to salinity. Farmers' willingness to pay for water varies, exceeding US 0.14/m³. These findings underscore the importance of understanding crop-specific responses to TWW blending and emphasize a holistic approach that considers both economic viability and environmental impacts for sustainable agricultural practices. Doi: 10.28991/HIJ-2025-06-01-016 Full Text: PD
Virtual Reality Tourism: Connecting Immersive Experiences to Future Travel Choices
Full text linkThis empirical investigation examines the transformative impact of Virtual Reality (VR) implementations on destination accessibility within China's tourism sector. The research examines the interrelated relationships between VR Experience (VREX) determinants, experiential outcomes, and subsequent Visiting Intention (VIS). The theoretical framework encompasses three fundamental VREX antecedents: Telepresence (TLP), VR Application Quality (VAQ), and Perceived Realism (PREA). This research analyzes the impact of VREX on Perceived Enjoyment (PE) and Perceived Advantage (PAD), and in turn, their impact on VIS. Through purposive sampling methodology, the study gathered responses from 307 individuals actively engaging with VR tourism applications across China. Statistical analysis revealed significant associations between VREX and VIS, with all three antecedents demonstrating substantial influence on VREX formation. The findings establish that VREX has a significant impact on both PE and PAD dimensions. Notably, while PE emerged as a significant determinant of VIS, PAD demonstrated no substantial effect on visit intentions. This investigation advances theoretical discourse in virtual tourism by illuminating the crucial role of immersive technological experiences in destination marketing. For practitioners, these findings suggest prioritizing enjoyment-focused VR designs and investing in technologies that enhance telepresence and realism to influence potential tourists' visit intentions effectively
Public Opinion Guidance Model in Major Public Crisis Events Based on Accelerated Genetic Algorithm
Full text linkThe research aims to enhance the effectiveness of the public opinion evolution guidance model, with a particular focus on the influence of opinion leaders, and address the shortcomings of traditional models, such as neglecting opinion leaders and insufficient network topology. Therefore, the relevant scale-free network is used to improve the traditional public opinion evolution model. An improved model integrating the two is proposed by introducing a real coded accelerated genetic algorithm. The experimental results show that the proposed model converges to four opinion clusters, with the average values of negative and positive opinions being 0.399 and 0.370, respectively, demonstrating the trend closest to the actual data. When the parameters are fixed, the ultimate development of public opinion shows obvious changing trends in different situations, and the validity of the model has been proved by practice. The research innovatively introduces the scale-free network based on Barabasi and Albert, and improves the Hegselmann Krause model. Meanwhile, by comprehensively considering the influence of opinion leaders and network topology factors, the model overcomes the shortcomings of traditional models in public opinion guidance and also demonstrates good practicability in practical applications